add

agentgraph/methods/production/openai_structured_extractor.py

@@ -7,6 +7,7 @@ knowledge graphs in one step using Pydantic models.
 """
 
 import os
+import json
 import logging
 from typing import Optional, List, Dict, Any
 import uuid
@@ -18,6 +19,15 @@ from pydantic import BaseModel
 # Import Pydantic models
 from agentgraph.shared.models.reference_based import KnowledgeGraph, Entity, Relation, ContentReference
 
+# CoT reasoning models
+class Step(BaseModel):
+    explanation: str
+    output: str
+
+class KnowledgeGraphReasoning(BaseModel):
+    steps: List[Step]
+    final_answer: KnowledgeGraph
+
 # Load environment variables from root directory
 load_dotenv('/Users/zekunwu/Desktop/agent_monitoring/.env')
 
@@ -30,7 +40,7 @@ class OpenAIStructuredExtractor:
     Simple knowledge graph extractor using OpenAI's structured outputs.
     """
     
-    def __init__(self, model: str = "gpt-4o-2024-08-06"):
+    def __init__(self, model: str = "gpt-5-mini"):
         """
         Initialize the extractor.
         
@@ -54,285 +64,110 @@ class OpenAIStructuredExtractor:
         """
         logger.info(f"Starting knowledge graph extraction for {len(input_data)} characters of input")
         
-        # System prompt - focus on your role and methodology
-        system_prompt = """You are an expert knowledge graph analyst specializing in agent system traces.
-
-Extract comprehensive knowledge graphs capturing all entities and their precise relationships. Focus on workflow accuracy and relationship completeness.
-
-CORE PRINCIPLES:
-1. Capture ALL participants: agents, tools, tasks, inputs, outputs, humans
-2. Use professional naming (spaces, not underscores)
-3. Map complete workflows: Input → Agents perform Tasks → Output → Human
-4. Connect sequential tasks with NEXT relationships
-5. Show tool dependencies with REQUIRED_BY relationships
-6. Identify failures: errors, mistakes, broken processes, incorrect outputs
-7. Suggest optimizations: improvements, efficiency gains, better approaches
-
-RELATIONSHIP TYPES (use exactly these):
-- CONSUMED_BY: Input consumed by agent
-- PERFORMS: Agent performs task  
-- NEXT: Task A leads to Task B (critical for workflow)
-- PRODUCES: Task produces output
-- DELIVERS_TO: Output delivered to human
-- REQUIRED_BY: Task needs tool to execute (not USES)
-- USES: Agent uses tool for general support
-
-Return complete, accurate knowledge graphs with proper workflow sequences."""
-
-        # User prompt - specific instructions with few-shot example and data
-        user_prompt = f"""Analyze this agent system trace and extract a knowledge graph with the following specifications:
-
-EXAMPLE OUTPUT STRUCTURE:
-Here's the expected knowledge graph structure for multi-agent collaboration traces:
-{{
-  "system_name": "California Great America Ticket Analysis System",
-  "system_summary": "This system helps analyze the cost-saving potential of purchasing season passes versus individual daily tickets at California's Great America in San Jose. The process starts with an inquiry regarding savings from the Inquiry about Savings from Season Pass vs Daily Tickets (input_001), which is consumed by the Verification Expert (agent_002), who performs the Verify Cost of Daily Ticket and Season Pass in 2024 (task_001).",
-  "entities": [
-    {{
-      "id": "agent_001",
-      "type": "Agent",
-      "name": "Problem Solving Expert",
-      "importance": "HIGH",
-      "raw_prompt": "You are given: (1) a task and advises from your manager with a specific plan and (2) a general task. Collect information from the general task, follow the suggestions from manager to solve the task.",
-      "raw_prompt_ref": [{{"line_start": 17, "line_end": 17}}, {{"line_start": 34, "line_end": 34}}, {{"line_start": 45, "line_end": 45}}]
-    }},
-    {{
-      "id": "agent_002",
-      "type": "Agent",
-      "name": "Verification Expert",
-      "importance": "HIGH",
-      "raw_prompt": "To solve the task and verify the provided costs for a daily ticket and a season pass for California's Great America in San Jose for the summer of 2024, I will follow the steps mentioned in the plan.",
-      "raw_prompt_ref": [{{"line_start": 66, "line_end": 66}}, {{"line_start": 112, "line_end": 112}}, {{"line_start": 149, "line_end": 149}}, {{"line_start": 164, "line_end": 164}}]
-    }},
-    {{
-      "id": "agent_003",
-      "type": "Agent",
-      "name": "Arithmetic Progressions Expert",
-      "importance": "MEDIUM",
-      "raw_prompt": "Thank you for the thorough verification, Verification_Expert. Given the verified costs and calculation of savings.",
-      "raw_prompt_ref": [{{"line_start": 172, "line_end": 172}}, {{"line_start": 181, "line_end": 181}}]
-    }},
-    {{
-      "id": "agent_004",
-      "type": "Tool",
-      "name": "Computer Terminal",
-      "importance": "LOW",
-      "raw_prompt": "Code execution environment and computational terminal for running calculations, scripts, and data processing tasks. Provides computational support to other agents when code execution is required.",
-      "raw_prompt_ref": [{{"line_start": 21, "line_end": 21}}, {{"line_start": 32, "line_end": 32}}]
-    }},
-    {{
-      "id": "task_001",
-      "type": "Task",
-      "name": "Verify Cost of Daily Ticket and Season Pass in 2024",
-      "importance": "HIGH",
-      "raw_prompt": "Verify the accuracy of the provided costs for a daily ticket and a season pass for California's Great America in San Jose for the summer of 2024.",
-      "raw_prompt_ref": [{{"line_start": 8, "line_end": 8}}, {{"line_start": 10, "line_end": 10}}, {{"line_start": 11, "line_end": 12}}]
-    }},
-    {{
-      "id": "task_002",
-      "type": "Task",
-      "name": "Calculate Savings Amount",
-      "importance": "HIGH",
-      "raw_prompt": "Calculate the amount saved by purchasing a season pass instead of daily tickets for 4 visits.",
-      "raw_prompt_ref": [{{"line_start": 119, "line_end": 126}}]
-    }},
-    {{
-      "id": "input_001",
-      "type": "Input",
-      "name": "Inquiry about Savings from Season Pass vs Daily Tickets",
-      "importance": "HIGH",
-      "raw_prompt": "How much did I save by purchasing a season pass instead of daily tickets for California's Great America in San Jose, if I planned to visit once a month in June, July, August, and September during the summer of 2024?",
-      "raw_prompt_ref": [{{"line_start": 6, "line_end": 6}}]
-    }},
-    {{
-      "id": "output_001",
-      "type": "Output",
-      "name": "Saved Amount from Season Pass Purchase",
-      "importance": "HIGH",
-      "raw_prompt": "Amount saved = Total cost of daily tickets - Cost of season pass. Amount saved = $240 - $120 = $120. So, you saved $120 by purchasing a season pass instead of daily tickets.",
-      "raw_prompt_ref": [{{"line_start": 119, "line_end": 119}}, {{"line_start": 126, "line_end": 126}}]
-    }},
-    {{
-      "id": "human_001",
-      "type": "Human",
-      "name": "Park Visitor",
-      "importance": "HIGH",
-      "raw_prompt": "Person inquiring about ticket cost savings for California's Great America visits",
-      "raw_prompt_ref": [{{"line_start": 1, "line_end": 1}}]
-    }}
-  ],
-  "relations": [
-    {{
-      "id": "relation_001",
-      "source": "input_001",
-      "target": "agent_002",
-      "type": "CONSUMED_BY",
-      "importance": "HIGH",
-      "interaction_prompt": "Input inquiry is consumed by the Verification Expert for processing and analysis",
-      "interaction_prompt_ref": [{{"line_start": 6, "line_end": 6}}]
-    }},
-    {{
-      "id": "relation_002",
-      "source": "agent_002",
-      "target": "task_001",
-      "type": "PERFORMS",
-      "importance": "HIGH",
-      "interaction_prompt": "Verification Expert performs the task of verifying ticket costs and calculating savings",
-      "interaction_prompt_ref": [{{"line_start": 112, "line_end": 112}}, {{"line_start": 164, "line_end": 164}}]
-    }},
-    {{
-      "id": "relation_003",
-      "source": "task_001",
-      "target": "agent_002",
-      "type": "ASSIGNED_TO",
-      "importance": "HIGH",
-      "interaction_prompt": "Task is assigned to Verification Expert for execution",
-      "interaction_prompt_ref": [{{"line_start": 8, "line_end": 8}}]
-    }},
-    {{
-      "id": "relation_004",
-      "source": "task_001",
-      "target": "output_001",
-      "type": "PRODUCES",
-      "importance": "HIGH",
-      "interaction_prompt": "Task produces the final output showing the amount saved from season pass purchase",
-      "interaction_prompt_ref": [{{"line_start": 119, "line_end": 119}}]
-    }},
-    {{
-      "id": "relation_005",
-      "source": "output_001",
-      "target": "human_001",
-      "type": "DELIVERS_TO",
-      "importance": "HIGH",
-      "interaction_prompt": "Final calculation results are delivered to the park visitor",
-      "interaction_prompt_ref": [{{"line_start": 126, "line_end": 126}}]
-    }},
-    {{
-      "id": "relation_006",
-      "source": "task_001",
-      "target": "task_002",
-      "type": "NEXT",
-      "importance": "HIGH",
-      "interaction_prompt": "Verification task leads to arithmetic calculation task",
-      "interaction_prompt_ref": [{{"line_start": 164, "line_end": 164}}]
-    }},
-    {{
-      "id": "relation_007",
-      "source": "task_002",
-      "target": "agent_004",
-      "type": "REQUIRED_BY",
-      "importance": "MEDIUM",
-      "interaction_prompt": "Calculation task requires computer terminal for execution",
-      "interaction_prompt_ref": [{{"line_start": 50, "line_end": 55}}]
-    }}
-  ],
-  "failures": [
-    {{
-      "id": "failure_001",
-      "description": "Verification Expert failed to access real-time pricing data, relying on potentially outdated cost estimates",
-      "raw_text": "However, since I am currently unable to access external websites, I will use the provided cost",
-      "raw_text_ref": [],
-      "affected_id": "agent_002",
-      "risk_type": "RETRIEVAL_ERROR"
-    }}
-  ],
-  "optimizations": [
-    {{
-      "id": "opt_001",
-      "description": "Implement automated price verification system to reduce manual verification overhead and improve accuracy",
-      "raw_text": "Enhanced price verification with real-time data access",
-      "raw_text_ref": [],
-      "affected_ids": ["agent_002", "task_001"],
-      "recommendation_type": "TOOL_ENHANCEMENT"
-    }}
-  ]
-}}
-
-Now analyze the following trace data:
-
-ENTITY TYPES:
-- Agent: AI agents with specific roles
-- Task: Specific tasks or objectives  
-- Tool: Tools or functions used
-- Input: Data inputs to the system
-- Output: Data outputs from the system
-- Human: Human users or stakeholders
-
-RELATIONSHIP TYPES:
-- CONSUMED_BY: Input→Agent
-- PERFORMS: Agent→Task  
-- ASSIGNED_TO: Task→Agent
-- USES: Agent→Tool
-- REQUIRED_BY: Tool→Task
-- SUBTASK_OF: Task→Task
-- NEXT: Task→Task (sequence)
-- PRODUCES: Task→Output
-- DELIVERS_TO: Output→Human
-- INTERVENES: Agent/Human→Task
-
-REQUIREMENTS:
-For each entity provide:
-- id: unique identifier
-- type: one of the types above
-- name: descriptive name
-- importance: HIGH, MEDIUM, or LOW
-- raw_prompt: actual prompt/specification content that defines this entity
-- raw_prompt_ref: list of content references ONLY if <L#> markers are present in the input. If no <L#> markers, use empty list []
-
-For each relation provide:
-- id: unique identifier
-- source/target: entity IDs
-- type: one of the types above
-- importance: HIGH, MEDIUM, or LOW
-- interaction_prompt: runtime evidence showing this relationship occurred
-- interaction_prompt_ref: list of content references ONLY if <L#> markers are present in the input. If no <L#> markers, use empty list []
-
-IMPORTANT: Only create content references when you see explicit <L#> line markers in the input text. Do not invent line numbers.
-
-Also provide system_name and system_summary for the overall system.
-
-EXTRACTION FOCUS:
-1. Identify ALL named participants (agents, tools, tasks, inputs, outputs, human)
-2. Create sequential task chains: Task1 NEXT Task2 NEXT Task3
-3. Show tool dependencies: Task REQUIRED_BY Tool (when task needs tool to execute)
-4. Use clean professional naming (no underscores)
-5. Complete workflow: Input CONSUMED_BY Agent PERFORMS Task PRODUCES Output DELIVERS_TO Human
-6. DETECT FAILURES: Look for errors, exceptions, incorrect results, failed executions, incomplete tasks, missing validations
-7. SUGGEST OPTIMIZATIONS: Identify inefficiencies, redundancies, improvement opportunities, missing tools, workflow enhancements
-
-CRITICAL: Use NEXT for task sequences, REQUIRED_BY for tool dependencies.
-
-FAILURE DETECTION (look for):
-- Execution errors, exceptions, failed operations
-- Incorrect outputs, wrong calculations, invalid results  
-- Missing validations, incomplete processes
-- Tool limitations, access restrictions
-- Agent coordination problems
-
-OPTIMIZATION OPPORTUNITIES (suggest):
-- Tool enhancements, automation possibilities
-- Workflow simplifications, redundancy removal
-- Agent merging or specialization
-- Performance improvements, efficiency gains
-- Missing capabilities or better approaches
-
-IMPORTANT: Always provide at least 1-2 failures and optimizations based on trace analysis.
+        # System prompt - Optimized and streamlined
+        system_prompt = """You are an expert knowledge graph analyst specialized in AgentGraph extraction from JSON traces.
+
+== CORE EXTRACTION PRINCIPLES ==
+
+ENTITY TYPES & PRIORITIES:
+- Agent: Specialized roles (*_Expert, *_Specialist) - HIGH PRIORITY
+- Task: Discrete high-level operations (max 3 for complex workflows) - HIGH PRIORITY  
+- Tool: Computer Terminal, APIs, databases (Computer* = Tool, not Agent) - MEDIUM PRIORITY
+- Input/Output: Workflow start/end points - HIGH PRIORITY
+- Human: End users and stakeholders - HIGH PRIORITY
+
+RELATION PRIORITIES:
+- PERFORMS (Agent→Task): CRITICAL for all workflows
+- NEXT (Task→Task): CRITICAL for 3+ agent workflows  
+- CONSUMED_BY/PRODUCES/DELIVERS_TO: HIGH for workflow flow
+- USES/REQUIRED_BY: MEDIUM for tool dependencies
+
+WORKFLOW PATTERNS:
+- Simple (1-2 agents): Single consolidated task, basic relations
+- Complex (3+ agents): 3 high-level specialized tasks with NEXT relations (avoid over-decomposition)
+
+JSON PARSING FOCUS:
+Locate "observations", "agents", "data" sections. Count DISTINCT agents (not mentions).
+Look for *_Expert patterns and execution evidence.
+
+OUTPUT REQUIREMENTS:
+- Professional domain-specific naming
+- Complete workflow: Input→Agent→Task→Output→Human
+- ID format: agent_001, task_001, etc.
+- Empty raw_prompt/interaction_prompt fields
+- Include 1-2 failures and optimizations"""
+
+        # User prompt - Streamlined and focused
+        user_prompt = f"""Extract a knowledge graph from this trace using systematic reasoning steps.
+
+ANALYSIS STEPS:
+
+1. JSON STRUCTURE ANALYSIS:
+   - Count DISTINCT agents in "observations"/"agents" sections
+   - Identify domain and MAIN GOAL (single verification task vs multi-step process)
+   - Decide task structure:
+     * UNIFIED GOAL (verification/analysis/inquiry): 1 task, multiple collaborating agents
+       Example: "Verify Season Pass Savings" with Problem Solving Expert + Verification Expert
+     * SEQUENTIAL PROCESS (location→search→filter): 2-3 tasks with NEXT relations
+       Example: "Geographic Analysis" → "Data Collection" → "Validation"
+
+2. ENTITY EXTRACTION:
+   - Agents: Look for *_Expert, *_Specialist patterns (exclude Computer*)
+   - Tasks: ADAPTIVE based on workflow nature:
+     * Single goal/unified purpose: 1 consolidated task (multiple agents collaborate)
+     * Multi-step sequential process: 2-3 specialized tasks (each with clear dependencies)
+   - Tools: Computer Terminal/APIs/databases (Computer* = Tool type)
+   - Input/Output: Single workflow start/end points
+   - Human: End users receiving outputs
+
+3. RELATION MAPPING:
+   - PERFORMS: ADAPTIVE mapping:
+     * Simple workflows: Multiple agents→1 consolidated task
+     * Complex workflows: Each agent→specialized task OR multiple agents→shared task
+   - NEXT: Task→task only when tasks are sequential (max 2 NEXT relations)
+   - CONSUMED_BY/PRODUCES/DELIVERS_TO: Single workflow flow
+   - USES/REQUIRED_BY: Essential tool connections only
+
+4. QUALITY CHECK:
+   - Verify all relation IDs reference existing entities
+   - Ensure complete workflow: Input→Agent→Task→Output→Human
+   - Include 1-2 failures and optimizations
+
+FORMATTING:
+- IDs: agent_001, task_001, tool_001, etc.
+- Empty raw_prompt and interaction_prompt fields
+- Line number references only in _ref fields
 
 TRACE DATA:
 {input_data}"""
         
         try:
-            response = self.client.responses.parse(
+            response = self.client.beta.chat.completions.parse(
                 model=self.model,
-                input=[
+                messages=[
                     {"role": "system", "content": system_prompt},
                     {"role": "user", "content": user_prompt}
                 ],
-                text_format=KnowledgeGraph,
+                response_format=KnowledgeGraphReasoning,
             )
             
-            # Get the parsed response directly as KnowledgeGraph
-            knowledge_graph = response.output_parsed
+            # Get the parsed response with reasoning steps
+            logger.info("Parsing OpenAI response...")
+            reasoning_result = response.choices[0].message.parsed
+            logger.info(f"Response parsed successfully. Type: {type(reasoning_result)}")
+            
+            knowledge_graph = reasoning_result.final_answer
+            logger.info(f"Knowledge graph extracted. Type: {type(knowledge_graph)}")
+            
+            # Save reasoning steps and knowledge graph for analysis
+            logger.info("About to save extraction results...")
+            self._save_extraction_results(reasoning_result, input_data)
+            logger.info("Save extraction results completed.")
+            
+            # Log reasoning steps for debugging
+            logger.info("CoT Reasoning Steps:")
+            for step in reasoning_result.steps:
+                logger.info(f"Step: {step.explanation}")
             
             logger.info(f"Extraction complete: {len(knowledge_graph.entities)} entities, {len(knowledge_graph.relations)} relations")
             return knowledge_graph
@@ -340,6 +175,57 @@ TRACE DATA:
         except Exception as e:
             logger.error(f"Extraction failed: {e}")
             raise
+    
+    def _save_extraction_results(self, reasoning_result, trace_text: str):
+        """Save reasoning steps and knowledge graph for analysis"""
+        try:
+            logger.info("Starting to save extraction results...")
+            
+            # Create results directory if it doesn't exist
+            results_dir = "extraction_analysis"
+            os.makedirs(results_dir, exist_ok=True)
+            logger.info(f"Results directory ready: {results_dir}")
+            
+            # Generate timestamp and unique ID
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            extraction_id = str(uuid.uuid4())[:8]
+            filename = f"cot_extraction_{timestamp}_{extraction_id}.json"
+            logger.info(f"Generated filename: {filename}")
+            
+            # Prepare data to save using model_dump for safe serialization
+            logger.info("Serializing reasoning steps...")
+            reasoning_steps_data = [step.model_dump() for step in reasoning_result.steps]
+            logger.info(f"Reasoning steps serialized: {len(reasoning_steps_data)} steps")
+            
+            logger.info("Serializing knowledge graph...")
+            kg_data = reasoning_result.final_answer.model_dump()
+            logger.info(f"Knowledge graph serialized: {kg_data.get('system_name', 'Unknown')}")
+            
+            save_data = {
+                "timestamp": timestamp,
+                "extraction_id": extraction_id,
+                "model": self.model,
+                "reasoning_steps": reasoning_steps_data,
+                "knowledge_graph": kg_data,
+                "input_trace_length": len(trace_text),
+                "input_trace_preview": trace_text[:500] + "..." if len(trace_text) > 500 else trace_text
+            }
+            
+            # Save to file
+            filepath = os.path.join(results_dir, filename)
+            logger.info(f"Writing to file: {filepath}")
+            
+            with open(filepath, 'w', encoding='utf-8') as f:
+                json.dump(save_data, f, indent=2, ensure_ascii=False)
+            
+            # Verify file was written
+            file_size = os.path.getsize(filepath)
+            logger.info(f"Extraction results saved successfully: {filepath} ({file_size} bytes)")
+            
+        except Exception as e:
+            logger.error(f"Failed to save extraction results: {e}")
+            import traceback
+            logger.error(f"Traceback: {traceback.format_exc()}")
 
     def process_text(self, input_data: str) -> Dict[str, Any]:
         """
@@ -367,7 +253,7 @@ TRACE DATA:
 def extract_knowledge_graph_with_context(
     input_data: str,
     context_documents: Optional[List[Dict[str, Any]]] = None,
-    model: str = "gpt-4o-2024-08-06"
+    model: str = "gpt-5-mini"
 ) -> KnowledgeGraph:
     """
     Main entry point for knowledge graph extraction.

agentgraph/methods/production/openai_structured_extractor_backup_20250907_161109.py

@@ -0,0 +1,548 @@
+#!/usr/bin/env python3
+"""
+OpenAI Structured Outputs Knowledge Extractor
+
+A simple, direct approach using OpenAI's structured outputs API to extract
+knowledge graphs in one step using Pydantic models.
+"""
+
+import os
+import json
+import logging
+from typing import Optional, List, Dict, Any
+import uuid
+from datetime import datetime
+from dotenv import load_dotenv
+from openai import OpenAI
+from pydantic import BaseModel
+
+# Import Pydantic models
+from agentgraph.shared.models.reference_based import KnowledgeGraph, Entity, Relation, ContentReference
+
+# CoT reasoning models
+class Step(BaseModel):
+    explanation: str
+    output: str
+
+class KnowledgeGraphReasoning(BaseModel):
+    steps: List[Step]
+    final_answer: KnowledgeGraph
+
+# Load environment variables from root directory
+load_dotenv('/Users/zekunwu/Desktop/agent_monitoring/.env')
+
+# Configure logging
+logger = logging.getLogger(__name__)
+
+
+class OpenAIStructuredExtractor:
+    """
+    Simple knowledge graph extractor using OpenAI's structured outputs.
+    """
+    
+    def __init__(self, model: str = "gpt-5-mini"):
+        """
+        Initialize the extractor.
+        
+        Args:
+            model: OpenAI model to use (must support structured outputs)
+        """
+        self.model = model
+        self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+        logger.info(f"OpenAI Structured Extractor initialized with model: {model}")
+    
+    def extract_knowledge_graph(self, input_data: str, context_documents: Optional[List[Dict[str, Any]]] = None) -> KnowledgeGraph:
+        """
+        Extract knowledge graph from input data using OpenAI structured outputs.
+        
+        Args:
+            input_data: The trace data to analyze
+            context_documents: Optional context documents (unused in this simple version)
+            
+        Returns:
+            KnowledgeGraph: Extracted knowledge graph
+        """
+        logger.info(f"Starting knowledge graph extraction for {len(input_data)} characters of input")
+        
+        # System prompt - focus on your role and methodology
+        system_prompt = """You are an expert knowledge graph analyst specializing in agent system traces.
+
+Extract comprehensive knowledge graphs capturing all entities and their precise relationships. Focus on workflow accuracy and relationship completeness.
+
+CORE PRINCIPLES:
+1. Capture ALL participants: agents, tools, tasks, inputs, outputs, humans
+2. Use professional naming (spaces, not underscores)
+3. Map complete workflows: Input → Agents perform Tasks → Output → Human
+4. Focus on MAIN workflow tasks (avoid over-decomposition into subtasks)
+5. Use NEXT only for clear sequential task dependencies
+6. Use REQUIRED_BY for essential tool dependencies (not USES for general support)
+7. Identify failures: errors, mistakes, broken processes, incorrect outputs
+8. Suggest optimizations: improvements, efficiency gains, better approaches
+
+ID ASSIGNMENT GUIDELINES:
+- Use sequential numbering starting from 001 for each entity type
+- agent_001, agent_002, agent_003... (in order of appearance/importance)
+- task_001, task_002, task_003... (in workflow order)
+- input_001, output_001, human_001 (typically one of each)
+- tool_001 for Computer Terminal or similar tools
+- Assign based on trace content, not predetermined roles
+
+RELATIONSHIP TYPES (use exactly these):
+- CONSUMED_BY: Input consumed by agent (input_001 → agent_001)
+- PERFORMS: Agent performs task (agent_001 → task_001)  
+- NEXT: Task A leads to Task B in sequence (task_001 → task_002)
+- PRODUCES: Task produces output (task_001 → output_001)
+- DELIVERS_TO: Output delivered to human (output_001 → human_001)
+- REQUIRED_BY: Tool required by task (agent_004 → task_001)
+- USES: Agent uses tool (agent_001 → agent_004)
+- ASSIGNED_TO: Task assigned to agent (task_001 → agent_002)
+- INTERVENES: Agent coordinates task (agent_002 → task_001)
+
+Return complete, accurate knowledge graphs with proper workflow sequences."""
+
+        # User prompt - specific instructions with few-shot example and data
+        user_prompt = f"""Analyze this agent system trace step by step and extract a knowledge graph.
+
+Think through this systematically. Use as many reasoning steps as needed to thoroughly analyze the trace.
+Each step should have a clear explanation and specific output findings.
+
+IMPORTANT FORMATTING RULES:
+- Set raw_prompt to empty string "" for ALL entities (content should only be in raw_prompt_ref)
+- Set interaction_prompt to empty string "" for ALL relations (content should only be in interaction_prompt_ref)
+- Use only line number references in raw_prompt_ref and interaction_prompt_ref, never include actual text content
+- Determine your own number of reasoning steps based on trace complexity
+
+EXAMPLE OUTPUT STRUCTURE:
+Here's the expected knowledge graph structure for multi-agent collaboration traces:
+{{
+  "system_name": "California Great America Ticket Analysis System",
+  "system_summary": "This system helps analyze the cost-saving potential of purchasing season passes versus individual daily tickets at California's Great America in San Jose. The process starts with an inquiry regarding savings from the Inquiry about Savings from Season Pass vs Daily Tickets (input_001), which is consumed by the Verification Expert (agent_002), who performs the Verify Cost of Daily Ticket and Season Pass in 2024 (task_001).",
+  "entities": [
+    {{
+      "id": "agent_001",
+      "type": "Agent",
+      "name": "Problem Solving Expert",
+      "importance": "HIGH",
+      "raw_prompt": "",
+      "raw_prompt_ref": [{{"line_start": 17, "line_end": 17}}, {{"line_start": 34, "line_end": 34}}, {{"line_start": 45, "line_end": 45}}]
+    }},
+    {{
+      "id": "agent_002",
+      "type": "Agent",
+      "name": "Verification Expert",
+      "importance": "HIGH",
+      "raw_prompt": "",
+      "raw_prompt_ref": [{{"line_start": 66, "line_end": 66}}, {{"line_start": 112, "line_end": 112}}, {{"line_start": 149, "line_end": 149}}, {{"line_start": 164, "line_end": 164}}]
+    }},
+    {{
+      "id": "agent_003",
+      "type": "Agent",
+      "name": "Arithmetic Progressions Expert",
+      "importance": "MEDIUM",
+      "raw_prompt": "",
+      "raw_prompt_ref": [{{"line_start": 172, "line_end": 172}}, {{"line_start": 181, "line_end": 181}}]
+    }},
+    {{
+      "id": "agent_004",
+      "type": "Tool",
+      "name": "Computer Terminal",
+      "importance": "LOW",
+      "raw_prompt": "",
+      "raw_prompt_ref": [{{"line_start": 21, "line_end": 21}}, {{"line_start": 32, "line_end": 32}}]
+    }},
+    {{
+      "id": "task_001",
+      "type": "Task",
+      "name": "Verify Cost of Daily Ticket and Season Pass in 2024",
+      "importance": "HIGH",
+      "raw_prompt": "",
+      "raw_prompt_ref": [{{"line_start": 8, "line_end": 8}}, {{"line_start": 10, "line_end": 10}}, {{"line_start": 11, "line_end": 12}}]
+    }},
+    {{
+      "id": "task_002",
+      "type": "Task",
+      "name": "Calculate Savings Amount",
+      "importance": "HIGH",
+      "raw_prompt": "",
+      "raw_prompt_ref": [{{"line_start": 119, "line_end": 126}}]
+    }},
+    {{
+      "id": "input_001",
+      "type": "Input",
+      "name": "Inquiry about Savings from Season Pass vs Daily Tickets",
+      "importance": "HIGH",
+      "raw_prompt": "",
+      "raw_prompt_ref": [{{"line_start": 6, "line_end": 6}}]
+    }},
+    {{
+      "id": "output_001",
+      "type": "Output",
+      "name": "Saved Amount from Season Pass Purchase",
+      "importance": "HIGH",
+      "raw_prompt": "",
+      "raw_prompt_ref": [{{"line_start": 119, "line_end": 119}}, {{"line_start": 126, "line_end": 126}}]
+    }},
+    {{
+      "id": "human_001",
+      "type": "Human",
+      "name": "Park Visitor",
+      "importance": "HIGH",
+      "raw_prompt": "",
+      "raw_prompt_ref": [{{"line_start": 1, "line_end": 1}}]
+    }}
+  ],
+  "relations": [
+    {{
+      "id": "relation_001",
+      "source": "input_001",
+      "target": "agent_002",
+      "type": "CONSUMED_BY",
+      "importance": "HIGH",
+      "interaction_prompt": "",
+      "interaction_prompt_ref": [{{"line_start": 6, "line_end": 6}}]
+    }},
+    {{
+      "id": "relation_002",
+      "source": "agent_002",
+      "target": "task_001",
+      "type": "PERFORMS",
+      "importance": "HIGH",
+      "interaction_prompt": "",
+      "interaction_prompt_ref": [{{"line_start": 112, "line_end": 112}}, {{"line_start": 164, "line_end": 164}}]
+    }},
+    {{
+      "id": "relation_003",
+      "source": "task_001",
+      "target": "agent_002",
+      "type": "ASSIGNED_TO",
+      "importance": "HIGH",
+      "interaction_prompt": "",
+      "interaction_prompt_ref": [{{"line_start": 8, "line_end": 8}}]
+    }},
+    {{
+      "id": "relation_004",
+      "source": "task_001",
+      "target": "output_001",
+      "type": "PRODUCES",
+      "importance": "HIGH",
+      "interaction_prompt": "",
+      "interaction_prompt_ref": [{{"line_start": 119, "line_end": 119}}]
+    }},
+    {{
+      "id": "relation_005",
+      "source": "output_001",
+      "target": "human_001",
+      "type": "DELIVERS_TO",
+      "importance": "HIGH",
+      "interaction_prompt": "",
+      "interaction_prompt_ref": [{{"line_start": 126, "line_end": 126}}]
+    }},
+    {{
+      "id": "relation_006",
+      "source": "task_001",
+      "target": "task_002",
+      "type": "NEXT",
+      "importance": "HIGH",
+      "interaction_prompt": "",
+      "interaction_prompt_ref": [{{"line_start": 164, "line_end": 164}}]
+    }},
+    {{
+      "id": "relation_007",
+      "source": "task_002",
+      "target": "agent_004",
+      "type": "REQUIRED_BY",
+      "importance": "MEDIUM",
+      "interaction_prompt": "",
+      "interaction_prompt_ref": [{{"line_start": 50, "line_end": 55}}]
+    }}
+  ],
+  "failures": [
+    {{
+      "id": "failure_001",
+      "description": "Verification Expert failed to access real-time pricing data, relying on potentially outdated cost estimates",
+      "raw_text": "However, since I am currently unable to access external websites, I will use the provided cost",
+      "raw_text_ref": [],
+      "affected_id": "agent_002",
+      "risk_type": "RETRIEVAL_ERROR"
+    }}
+  ],
+  "optimizations": [
+    {{
+      "id": "opt_001",
+      "description": "Implement automated price verification system to reduce manual verification overhead and improve accuracy",
+      "raw_text": "Enhanced price verification with real-time data access",
+      "raw_text_ref": [],
+      "affected_ids": ["agent_002", "task_001"],
+      "recommendation_type": "TOOL_ENHANCEMENT"
+    }}
+  ]
+}}
+
+Now analyze the following trace data:
+
+ENTITY TYPES:
+- Agent: AI agents with specific roles
+- Task: Specific tasks or objectives  
+- Tool: Tools or functions used
+- Input: Data inputs to the system
+- Output: Data outputs from the system
+- Human: Human users or stakeholders
+
+RELATIONSHIP TYPES:
+- CONSUMED_BY: Input→Agent
+- PERFORMS: Agent→Task  
+- ASSIGNED_TO: Task→Agent
+- USES: Agent→Tool
+- REQUIRED_BY: Tool→Task
+- SUBTASK_OF: Task→Task
+- NEXT: Task→Task (sequence)
+- PRODUCES: Task→Output
+- DELIVERS_TO: Output→Human
+- INTERVENES: Agent/Human→Task
+
+REQUIREMENTS:
+For each entity provide:
+- id: unique identifier
+- type: one of the types above
+- name: descriptive name
+- importance: HIGH, MEDIUM, or LOW
+- raw_prompt: actual prompt/specification content that defines this entity
+- raw_prompt_ref: list of content references pointing to relevant lines in the input trace where this entity is mentioned or defined
+
+For each relation provide:
+- id: unique identifier
+- source/target: entity IDs
+- type: one of the types above
+- importance: HIGH, MEDIUM, or LOW
+- interaction_prompt: runtime evidence showing this relationship occurred
+- interaction_prompt_ref: list of content references pointing to relevant lines in the input trace where this relationship evidence is found
+
+IMPORTANT: Create appropriate line number references based on where entities and relationships are mentioned in the trace. Use reasonable line estimates for JSON structure.
+
+Also provide system_name and system_summary for the overall system.
+
+EXTRACTION FOCUS:
+1. Identify MAIN participants: primary agents, core tasks, key inputs/outputs, humans
+2. Focus on the PRIMARY workflow (avoid creating too many subtasks)
+3. Connect entities based on ACTUAL interactions in the trace
+4. Use clean professional naming (no underscores)
+5. DETECT FAILURES: Look for errors, exceptions, incorrect results, failed executions
+6. SUGGEST OPTIMIZATIONS: Identify inefficiencies, improvement opportunities
+
+CRITICAL RELATIONSHIP PATTERNS (follow exactly):
+- input_001 --CONSUMED_BY--> agent_002 (main processing agent)
+- agent_002 --PERFORMS--> task_001 (primary task execution)
+- task_001 --ASSIGNED_TO--> agent_002 (task ownership)
+- task_001 --PRODUCES--> output_001 (direct output generation)
+- output_001 --DELIVERS_TO--> human_001 (final delivery)
+- agent_002 --INTERVENES--> task_001 (coordination/oversight)
+- agent_001 --USES--> agent_004 (tool utilization)
+
+ENTITY ID RULES:
+- agent_001: First mentioned expert, agent_002: Main performer, agent_003: Supporting expert
+- agent_004: Always Computer Terminal (tool type)
+- task_001: Primary workflow task (avoid creating task_002 unless truly sequential)
+- Use exactly these IDs, don't create tool_001 or extra tasks unnecessarily
+
+FAILURE DETECTION (look for):
+- Execution errors, exceptions, failed operations
+- Incorrect outputs, wrong calculations, invalid results  
+- Missing validations, incomplete processes
+- Tool limitations, access restrictions
+- Agent coordination problems
+
+OPTIMIZATION OPPORTUNITIES (suggest):
+- Tool enhancements, automation possibilities
+- Workflow simplifications, redundancy removal
+- Agent merging or specialization
+- Performance improvements, efficiency gains
+- Missing capabilities or better approaches
+
+IMPORTANT: Always provide at least 1-2 failures and optimizations based on trace analysis.
+
+TRACE DATA:
+{input_data}"""
+        
+        try:
+            response = self.client.responses.parse(
+                model=self.model,
+                input=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt}
+                ],
+                text_format=KnowledgeGraphReasoning,
+            )
+            
+            # Get the parsed response with reasoning steps
+            logger.info("Parsing OpenAI response...")
+            reasoning_result = response.output_parsed
+            logger.info(f"Response parsed successfully. Type: {type(reasoning_result)}")
+            
+            knowledge_graph = reasoning_result.final_answer
+            logger.info(f"Knowledge graph extracted. Type: {type(knowledge_graph)}")
+            
+            # Save reasoning steps and knowledge graph for analysis
+            logger.info("About to save extraction results...")
+            self._save_extraction_results(reasoning_result, input_data)
+            logger.info("Save extraction results completed.")
+            
+            # Log reasoning steps for debugging
+            logger.info("CoT Reasoning Steps:")
+            for step in reasoning_result.steps:
+                logger.info(f"Step: {step.explanation}")
+            
+            logger.info(f"Extraction complete: {len(knowledge_graph.entities)} entities, {len(knowledge_graph.relations)} relations")
+            return knowledge_graph
+            
+        except Exception as e:
+            logger.error(f"Extraction failed: {e}")
+            raise
+    
+    def _save_extraction_results(self, reasoning_result, trace_text: str):
+        """Save reasoning steps and knowledge graph for analysis"""
+        try:
+            logger.info("Starting to save extraction results...")
+            
+            # Create results directory if it doesn't exist
+            results_dir = "extraction_analysis"
+            os.makedirs(results_dir, exist_ok=True)
+            logger.info(f"Results directory ready: {results_dir}")
+            
+            # Generate timestamp and unique ID
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            extraction_id = str(uuid.uuid4())[:8]
+            filename = f"cot_extraction_{timestamp}_{extraction_id}.json"
+            logger.info(f"Generated filename: {filename}")
+            
+            # Prepare data to save using model_dump for safe serialization
+            logger.info("Serializing reasoning steps...")
+            reasoning_steps_data = [step.model_dump() for step in reasoning_result.steps]
+            logger.info(f"Reasoning steps serialized: {len(reasoning_steps_data)} steps")
+            
+            logger.info("Serializing knowledge graph...")
+            kg_data = reasoning_result.final_answer.model_dump()
+            logger.info(f"Knowledge graph serialized: {kg_data.get('system_name', 'Unknown')}")
+            
+            save_data = {
+                "timestamp": timestamp,
+                "extraction_id": extraction_id,
+                "model": self.model,
+                "reasoning_steps": reasoning_steps_data,
+                "knowledge_graph": kg_data,
+                "input_trace_length": len(trace_text),
+                "input_trace_preview": trace_text[:500] + "..." if len(trace_text) > 500 else trace_text
+            }
+            
+            # Save to file
+            filepath = os.path.join(results_dir, filename)
+            logger.info(f"Writing to file: {filepath}")
+            
+            with open(filepath, 'w', encoding='utf-8') as f:
+                json.dump(save_data, f, indent=2, ensure_ascii=False)
+            
+            # Verify file was written
+            file_size = os.path.getsize(filepath)
+            logger.info(f"Extraction results saved successfully: {filepath} ({file_size} bytes)")
+            
+        except Exception as e:
+            logger.error(f"Failed to save extraction results: {e}")
+            import traceback
+            logger.error(f"Traceback: {traceback.format_exc()}")
+
+    def process_text(self, input_data: str) -> Dict[str, Any]:
+        """
+        Process text and return structured response (for compatibility with extraction factory).
+        
+        Args:
+            input_data: The trace data to analyze
+            
+        Returns:
+            Dict with success status and kg_data
+        """
+        try:
+            kg = self.extract_knowledge_graph(input_data)
+            return {
+                "success": True,
+                "kg_data": kg.model_dump()
+            }
+        except Exception as e:
+            return {
+                "success": False,
+                "error": str(e),
+                "kg_data": {}
+            }
+
+def extract_knowledge_graph_with_context(
+    input_data: str,
+    context_documents: Optional[List[Dict[str, Any]]] = None,
+    model: str = "gpt-5-mini"
+) -> KnowledgeGraph:
+    """
+    Main entry point for knowledge graph extraction.
+    
+    Args:
+        input_data: The trace data to analyze
+        context_documents: Optional context documents
+        model: OpenAI model to use
+        
+    Returns:
+        KnowledgeGraph: Extracted knowledge graph
+    """
+    extractor = OpenAIStructuredExtractor(model=model)
+    return extractor.extract_knowledge_graph(input_data, context_documents)
+
+def extract_knowledge_graph(input_data: str) -> KnowledgeGraph:
+    """
+    Simple entry point without context (for backward compatibility).
+    
+    Args:
+        input_data: The trace data to analyze
+        
+    Returns:
+        KnowledgeGraph: Extracted knowledge graph
+    """
+    return extract_knowledge_graph_with_context(input_data)
+
+# Factory class for integration
+class OpenAIStructuredFactory:
+    """Factory class for OpenAI structured extraction method."""
+    
+    def __init__(self, model: str = "gpt-5-mini"):
+        self.model = model
+        self.extractor = OpenAIStructuredExtractor(model)
+    
+    def set_model(self, model: str):
+        """Set the model for this factory."""
+        self.model = model
+        self.extractor = OpenAIStructuredExtractor(model)
+    
+    def process_text(self, input_data: str) -> Dict[str, Any]:
+        """Process text using the extractor."""
+        return self.extractor.process_text(input_data)
+    
+    def extract_knowledge_graph(self, input_data: str, context_documents: Optional[List[Dict[str, Any]]] = None) -> KnowledgeGraph:
+        """Extract knowledge graph using the current model."""
+        return self.extractor.extract_knowledge_graph(input_data, context_documents)
+
+# Export factory instance
+openai_structured_factory = OpenAIStructuredFactory()
+
+if __name__ == "__main__":
+    # Simple test
+    test_input = """
+    Agent: DataAnalyzer
+    Task: Analyze customer data and generate insights
+    Tool: pandas_analyzer
+    
+    The DataAnalyzer agent processes customer data using pandas_analyzer tool
+    to generate business insights for the marketing team.
+    """
+    
+    print("Testing OpenAI Structured Extractor...")
+    try:
+        kg = extract_knowledge_graph(test_input)
+        print(f"✅ Success! Extracted {len(kg.entities)} entities and {len(kg.relations)} relations")
+        print(f"System: {kg.system_name}")
+        print(f"Summary: {kg.system_summary}")
+    except Exception as e:
+        print(f"❌ Error: {e}")

extraction_analysis/cot_extraction_20250907_154815_c47ad21a.json

@@ -0,0 +1,401 @@
+{
+  "timestamp": "20250907_154815",
+  "extraction_id": "c47ad21a",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Step 1 — Identify the primary input and human stakeholder from the trace.",
+      "output": "Found the general task (request to count nonindigenous crocodiles in Florida 2000–2020) as the main input and a manager (group chat manager) as the human stakeholder. (References: trace item 1 and item 6)."
+    },
+    {
+      "explanation": "Step 2 — Enumerate agents and tools that participate in the workflow and map their appearances.",
+      "output": "Identified four primary agents/tools: Statistical Analysis Expert (assistant who provided the plan), Data Analysis Expert (user posting the plan/steps and performing exploration), Data Verification Expert (user recommending verification steps), and Computer Terminal (execution environment/tool that ran commands and returned outputs). (References: trace items 1, 2, 4, 3, 5, 6)."
+    },
+    {
+      "explanation": "Step 3 — Identify the main task and the expected output format.",
+      "output": "Primary task: Determine the number of nonindigenous crocodiles found in Florida from 2000 through 2020 (task objective and plan). Expected output: a single numeric count reported in the requested output format. (References: trace items 1 and 2)."
+    },
+    {
+      "explanation": "Step 4 — Extract concrete interactions and map the primary workflow relations with evidence lines.",
+      "output": "Mapped the primary workflow relations according to the trace: the input (general task) is consumed by the main performer (Data Analysis Expert); Data Analysis Expert performs and is assigned the primary task and intervenes/coordinates it; the primary task is expected to produce the numeric output which is to be delivered to the manager; Statistical Analysis Expert and Data Analysis Expert both use the Computer Terminal (evidence: code execution attempts and shell commands). (Evidence references: trace items 1, 2, 3, 5, 7, 9)."
+    },
+    {
+      "explanation": "Step 5 — Detect failures and errors present in the trace.",
+      "output": "Detected multiple failures: (1) CSV parsing error from pandas when attempting to explore the file (parser/tokenization error), (2) downloaded file appears to be HTML (placeholder URL downloaded an HTML page instead of CSV), and (3) curl failure (could not resolve host) when attempting to fetch a URL. These failures indicate retrieval and execution problems affecting data validity. (References: trace items 3, 5, 9)."
+    },
+    {
+      "explanation": "Step 6 — Recommend optimizations and improvements to prevent recurrence of these failures and to streamline the workflow.",
+      "output": "Suggested optimizations include automated URL and content-type validation (verify CSV MIME/content before parsing), explicit URL confirmation step (require manager or agent to confirm authoritative USGS dataset URL), centralized download-and-verify tool to standardize retrieval and reduce repeated manual checks, and improved error handling in the exploration step to detect HTML responses and surface actionable errors early. (References: trace items 1, 3, 5, 9)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "USGS Nonindigenous Crocodile Count Workflow",
+    "system_summary": "This multi-agent workflow is focused on determining the number of nonindigenous crocodiles found in Florida from 2000 through 2020 using the USGS Nonindigenous Aquatic Species database. The general task (input_001) is consumed by the Data Analysis Expert (agent_002), who performs the primary analysis task (task_001) using the Computer Terminal (agent_004); results are expected to be produced as a numeric output and delivered to the Group Chat Manager (human_001). The trace shows download and parsing failures that blocked completion.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Statistical Analysis Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 38
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Data Analysis Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 39,
+            "line_end": 74
+          },
+          {
+            "line_start": 159,
+            "line_end": 190
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Data Verification Expert",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 97,
+            "line_end": 120
+          },
+          {
+            "line_start": 143,
+            "line_end": 158
+          }
+        ]
+      },
+      {
+        "id": "agent_004",
+        "type": "Tool",
+        "name": "Computer Terminal",
+        "importance": "LOW",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 75,
+            "line_end": 96
+          },
+          {
+            "line_start": 121,
+            "line_end": 142
+          },
+          {
+            "line_start": 143,
+            "line_end": 158
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Determine number of nonindigenous crocodiles in Florida (2000–2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 38
+          },
+          {
+            "line_start": 39,
+            "line_end": 74
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "General task: count nonindigenous crocodiles in Florida 2000–2020",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 38
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Numeric count of nonindigenous crocodiles (Florida, 2000–2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 38
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Group Chat Manager",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 38
+          },
+          {
+            "line_start": 143,
+            "line_end": 158
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "relation_001",
+        "source": "input_001",
+        "target": "agent_002",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 39,
+            "line_end": 74
+          },
+          {
+            "line_start": 1,
+            "line_end": 38
+          }
+        ]
+      },
+      {
+        "id": "relation_002",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 39,
+            "line_end": 74
+          },
+          {
+            "line_start": 159,
+            "line_end": 190
+          }
+        ]
+      },
+      {
+        "id": "relation_003",
+        "source": "task_001",
+        "target": "agent_002",
+        "type": "ASSIGNED_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 38
+          }
+        ]
+      },
+      {
+        "id": "relation_004",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 38
+          }
+        ]
+      },
+      {
+        "id": "relation_005",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 38
+          }
+        ]
+      },
+      {
+        "id": "relation_006",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "INTERVENES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 39,
+            "line_end": 74
+          }
+        ]
+      },
+      {
+        "id": "relation_007",
+        "source": "agent_001",
+        "target": "agent_004",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 75,
+            "line_end": 96
+          },
+          {
+            "line_start": 121,
+            "line_end": 142
+          }
+        ]
+      },
+      {
+        "id": "relation_008",
+        "source": "agent_002",
+        "target": "agent_004",
+        "type": "USES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 75,
+            "line_end": 96
+          },
+          {
+            "line_start": 215,
+            "line_end": 236
+          },
+          {
+            "line_start": 121,
+            "line_end": 142
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "CSV parsing failed due to tokenization error during explore_csv (pandas ParserError).",
+        "raw_text": "pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 8, saw 2",
+        "raw_text_ref": [
+          {
+            "line_start": 75,
+            "line_end": 96
+          }
+        ],
+        "affected_id": "agent_004"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Downloaded file was HTML (example domain) instead of the expected CSV dataset, indicating an incorrect URL or placeholder page.",
+        "raw_text": "head output shows an HTML document (Example Domain) rather than CSV content",
+        "raw_text_ref": [
+          {
+            "line_start": 121,
+            "line_end": 142
+          }
+        ],
+        "affected_id": "input_001"
+      },
+      {
+        "id": "failure_003",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Curl failed to download due to DNS/host resolution error (could not resolve host).",
+        "raw_text": "curl: (6) Could not resolve host: <URL>",
+        "raw_text_ref": [
+          {
+            "line_start": 215,
+            "line_end": 236
+          }
+        ],
+        "affected_id": "agent_004"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Implement automatic download validation: after fetching, verify content-type and check first N lines for CSV structure (comma-separated header) and reject HTML or error pages before parsing.",
+        "affected_ids": [
+          "agent_004",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 75,
+            "line_end": 96
+          },
+          {
+            "line_start": 121,
+            "line_end": 142
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require explicit confirmation/validation of the authoritative USGS CSV download URL (or allow manager to provide the exact URL) before download attempts.",
+        "affected_ids": [
+          "agent_002",
+          "input_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 1,
+            "line_end": 38
+          },
+          {
+            "line_start": 39,
+            "line_end": 74
+          }
+        ]
+      },
+      {
+        "id": "opt_003",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Centralize retrieval and verification into a single reusable subtask/tool (download-and-verify) to avoid repeated ad-hoc download attempts and to provide standardized error messages and fallbacks.",
+        "affected_ids": [
+          "agent_002",
+          "agent_004"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 39,
+            "line_end": 74
+          },
+          {
+            "line_start": 159,
+            "line_end": 190
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 9647,
+  "input_trace_preview": "[\n  {\n    \"content\": \"You are given: (1) a task and advises from your manager with a specific plan and (2) a general task.\\nCollect information from the general task, follow the suggestions from manager to solve the task.\\n\\n# General Task\\nHow many nonindigenous crocodiles were found in Florida from the year 2000 through 2020? You can get the data from the USGS Nonindigenous Aquatic Species database. Please solve the problem carefully.\\n\\n# Task and suggestions from manager\\n## Task description..."
+}

extraction_analysis/cot_extraction_20250907_154944_58fffc24.json

@@ -0,0 +1,391 @@
+{
+  "timestamp": "20250907_154944",
+  "extraction_id": "58fffc24",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Step 1 — Establish trace structure and map trace items to line ranges for references. I partitioned the 9 trace objects into sequential line ranges to create consistent references for raw_prompt_ref and interaction_prompt_ref.",
+      "output": "Assigned reasonable line ranges: item1 lines 1-20, item2 lines 21-40, item3 lines 41-100, item4 lines 101-140, item5 lines 141-160, item6 lines 161-180, item7 lines 181-200, item8 lines 201-220, item9 lines 221-230."
+    },
+    {
+      "explanation": "Step 2 — Identify main participants (agents, tool, human) from the trace and apply ID rules. The trace contains roles named: NorseMythology_Expert, Literary_Analysis_Expert, Computer_terminal, Verification_Expert. Per the ID rules: agent_001 is the first-mentioned expert, agent_002 is the main performer, agent_003 is the supporting/verification expert, agent_004 is the computer terminal tool.",
+      "output": "Mapped agents and tool: agent_001 -> NorseMythology Expert (first assistant), agent_002 -> Literary Analysis Expert (main performer), agent_003 -> Verification Expert (supporting), agent_004 -> Computer Terminal (tool)."
+    },
+    {
+      "explanation": "Step 3 — Identify primary task, inputs, outputs, and human stakeholder. The core task described repeatedly is verifying the quoted word in Emily Midkiff's June 2014 article in Fafnir. The input is the general task + manager suggestions. The output is the verified word quoted by two authors (the trace repeatedly states \"clichéd\"). The human stakeholder is the manager who provided the task instructions.",
+      "output": "task_001: Verify quoted word in Emily Midkiff's June 2014 Fafnir article (primary task). input_001: Manager-provided task and suggestions. output_001: The quoted word (\"clichéd\"). human_001: Manager."
+    },
+    {
+      "explanation": "Step 4 — Extract interactions/evidence from the trace to assemble relations. I identified where consumption, task performance, assignments, tool usage, production and delivery are evidenced, using the mapped line ranges.",
+      "output": "Key relation mapping (evidence lines): input_001 consumed by agent_002 (lines 1-20 & 21-40), agent_002 performs task_001 (lines 21-40 & 81-100), task_001 assigned to agent_002 (lines 1-20 & 21-40), task_001 produces output_001 (lines 1-20 & 161-180), output_001 delivered to human_001 (lines 161-180 & 221-230), agent_002 intervenes on task_001 (lines 21-40 & 141-160), agent_001 uses agent_004 (lines 1-20 & 41-100)."
+    },
+    {
+      "explanation": "Step 5 — Detect failures and risks in the workflow. I searched the trace for tool limitations, failed retrievals, and unverified assertions. The arXiv search did not find the target article (tool mismatch). The system then suggests manual verification and ultimately repeatedly asserts the word \"clichéd\" while lacking direct access to the journal article — this is a verification gap and risk of hallucination.",
+      "output": "Detected failures: (1) Retrieval limitation: arXiv search did not locate the Fafnir article (tool coverage mismatch). (2) Verification / Hallucination risk: repeated definitive assertion of \"clichéd\" without direct source access."
+    },
+    {
+      "explanation": "Step 6 — Formulate optimization recommendations to address the failures and improve the workflow. I propose improvements such as integrating literature-specific search tools, exposing agents to academic databases or web access (with controls), and clarifying verification responsibilities to avoid unsupported final assertions.",
+      "output": "Optimizations: (1) Add academic database / web access tool or specialized bibliographic search tool. (2) Enforce explicit source verification step before final assertion. (3) Combine search and verification responsibilities or add automation to avoid redundant/manual steps."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Fafnir Article Verification System",
+    "system_summary": "A small multi-agent workflow to verify a quoted word in Emily Midkiff's June 2014 article in the journal Fafnir. The manager-provided task (input_001) is consumed by the main processing agent (agent_002), which attempts literature search via a computer terminal (agent_004) and coordinates verification with a Verification Expert (agent_003), ultimately producing the claimed result (output_001) delivered to the manager (human_001).",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Norse Mythology Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 20
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Literary Analysis Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 21,
+            "line_end": 40
+          },
+          {
+            "line_start": 101,
+            "line_end": 140
+          },
+          {
+            "line_start": 161,
+            "line_end": 180
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification Expert",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 141,
+            "line_end": 160
+          },
+          {
+            "line_start": 181,
+            "line_end": 200
+          },
+          {
+            "line_start": 201,
+            "line_end": 220
+          }
+        ]
+      },
+      {
+        "id": "agent_004",
+        "type": "Tool",
+        "name": "Computer Terminal",
+        "importance": "LOW",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 41,
+            "line_end": 100
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Verify quoted word in Emily Midkiff's June 2014 Fafnir article",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 20
+          },
+          {
+            "line_start": 21,
+            "line_end": 40
+          },
+          {
+            "line_start": 101,
+            "line_end": 140
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Manager task and plan to verify quoted word in Fafnir article",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 20
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified quoted word (\"clichéd\")",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 20
+          },
+          {
+            "line_start": 161,
+            "line_end": 180
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Manager",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 20
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "relation_001",
+        "source": "input_001",
+        "target": "agent_002",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 20
+          },
+          {
+            "line_start": 21,
+            "line_end": 40
+          }
+        ]
+      },
+      {
+        "id": "relation_002",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 21,
+            "line_end": 40
+          },
+          {
+            "line_start": 81,
+            "line_end": 100
+          }
+        ]
+      },
+      {
+        "id": "relation_003",
+        "source": "task_001",
+        "target": "agent_002",
+        "type": "ASSIGNED_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 20
+          },
+          {
+            "line_start": 21,
+            "line_end": 40
+          }
+        ]
+      },
+      {
+        "id": "relation_004",
+        "source": "agent_001",
+        "target": "agent_004",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 20
+          },
+          {
+            "line_start": 41,
+            "line_end": 100
+          }
+        ]
+      },
+      {
+        "id": "relation_005",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 161,
+            "line_end": 180
+          },
+          {
+            "line_start": 1,
+            "line_end": 20
+          }
+        ]
+      },
+      {
+        "id": "relation_006",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 161,
+            "line_end": 180
+          },
+          {
+            "line_start": 221,
+            "line_end": 230
+          }
+        ]
+      },
+      {
+        "id": "relation_007",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "INTERVENES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 21,
+            "line_end": 40
+          },
+          {
+            "line_start": 141,
+            "line_end": 160
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Search via arXiv did not locate Emily Midkiff's article in Fafnir — arXiv coverage mismatch for this journal.",
+        "raw_text": "It appears that the arxiv_search results did not yield the specific article by Emily Midkiff in the journal \"Fafnir.\" This is likely because \"Fafnir\" might not be covered by the arXiv database",
+        "raw_text_ref": [
+          {
+            "line_start": 41,
+            "line_end": 100
+          }
+        ],
+        "affected_id": "agent_004"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "HALLUCINATION",
+        "description": "Final assertion that the quoted word is \"clichéd\" is made without evidence of accessing the source article — verification gap and risk of unsupported claim.",
+        "raw_text": "Based on the previous response that already identified the word \"clich\u0000e9d,\" ... Therefore, the word quoted ... is \"clich\u0000e9d.\"",
+        "raw_text_ref": [
+          {
+            "line_start": 161,
+            "line_end": 180
+          },
+          {
+            "line_start": 201,
+            "line_end": 220
+          }
+        ],
+        "affected_id": "agent_002"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Integrate a literature-specific search tool (JSTOR/Project MUSE/Google Scholar connector) or enable controlled web access so agents can find articles in humanities journals not covered by arXiv.",
+        "affected_ids": [
+          "agent_004",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 41,
+            "line_end": 100
+          },
+          {
+            "line_start": 101,
+            "line_end": 140
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Enforce an explicit verified-source checkpoint before any final claim: require agent_002 to attach source text/links and agent_003 to confirm source access to reduce hallucination risk.",
+        "affected_ids": [
+          "agent_002",
+          "agent_003",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 141,
+            "line_end": 160
+          },
+          {
+            "line_start": 161,
+            "line_end": 180
+          }
+        ]
+      },
+      {
+        "id": "opt_003",
+        "recommendation_type": "AGENT_MERGING",
+        "description": "Combine literature search and verification roles into a single coordinated agent or add automation that performs both steps atomically to avoid repeated manual handoffs.",
+        "affected_ids": [
+          "agent_002",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 21,
+            "line_end": 40
+          },
+          {
+            "line_start": 141,
+            "line_end": 160
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 14822,
+  "input_trace_preview": "[\n  {\n    \"content\": \"You are given: (1) a task and advises from your manager with a specific plan and (2) a general task.\\nCollect information from the general task, follow the suggestions from manager to solve the task.\\n\\n# General Task\\nIn Emily Midkiff's June 2014 article in a journal named for the one of Hreidmar's sons that guarded his house, what word was quoted from two different authors in distaste for the nature of dragon depictions? Please solve the problem carefully.\\n\\n# Task and s..."
+}

extraction_analysis/cot_extraction_20250907_155052_aade2354.json

@@ -0,0 +1,400 @@
+{
+  "timestamp": "20250907_155052",
+  "extraction_id": "aade2354",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Step 1 — Identify primary actors and data items in the trace (user messages, search, inference, follow-up). I locate the user query (initial), the knowledge search step, the inference (assistant response), and the follow-up request with examples.",
+      "output": "Detected primary records at approximate trace lines: user query (lines 6-6), knowledge search (lines 17-18), inference/answer generation (lines 28-31), follow-up request (line 38) and examples response (line 39)."
+    },
+    {
+      "explanation": "Step 2 — Define main entities following the ID rules: first expert (system assistant), main performer, supporting retrieval expert, and the Computer Terminal tool. Also define the single primary task (explain list comprehensions + examples), the user input (combined initial+follow-up), the produced output (explanation + examples), and the human.",
+      "output": "Entities created: agent_001 (system Python Programming Assistant), agent_002 (Response Generator — main performer), agent_003 (Documentation Search Agent — supporting), agent_004 (Computer Terminal — tool), task_001 (Explain Python list comprehensions and provide examples), input_001 (User question about list comprehensions), output_001 (Explanation and examples), human_001 (Learner User). Relevant trace refs: input lines 6,17-18,28-31,38-39."
+    },
+    {
+      "explanation": "Step 3 — Map primary workflow relationships required by the specification: input consumed by main agent; main agent performs and is assigned the primary task; task produces output delivered to the human; main agent intervenes/coordinates the task.",
+      "output": "Primary relations instantiated with supporting evidence references: CONSUMED_BY input_001→agent_002 (line 6), PERFORMS agent_002→task_001 (lines 29,39), ASSIGNED_TO task_001→agent_002 (line 28), PRODUCES task_001→output_001 (lines 29,39), DELIVERS_TO output_001→human_001 (lines 29,39), INTERVENES agent_002→task_001 (line 29)."
+    },
+    {
+      "explanation": "Step 4 — Include supporting retrieval workflow: the documentation search step was executed and used the Computer Terminal. The supporting search agent contributed to the main task and the terminal is required by the task.",
+      "output": "Added relations: PERFORMS agent_003→task_001 (knowledge retrieval supporting the primary task) with evidence lines 17-18; USES agent_001→agent_004 (system assistant used the terminal) with evidence lines 17-18; REQUIRED_BY agent_004→task_001 (terminal required by retrieval/response) with evidence lines 17-18."
+    },
+    {
+      "explanation": "Step 5 — Detect failures / risks from the trace and justify them with references. The trace contains claims and concise coverage but lacks precise citations for the performance claim; also the assistant gives a brief caveat about readability but does not deeply discuss trade-offs (completeness).",
+      "output": "Two detected risks: (1) HALLUCINATION: Unreferenced numeric claim about performance ('20-30% faster') — evidence lines 39; (2) EXECUTION_ERROR (completeness): Limited discussion of trade-offs and edge cases (nested comprehensions, readability costs) — evidence lines 29,39. Both affect the main performer agent_002 and the primary task."
+    },
+    {
+      "explanation": "Step 6 — Suggest concrete optimizations to improve accuracy and workflow efficiency referencing affected entities.",
+      "output": "Recommendations: add automated citation/benchmarking capability for performance claims (Tool enhancement: agent_004 + agent_003), refine assistant prompt to require explicit citations and caveats (Prompt refinement: agent_002 + task_001), and consolidate retrieval+response into a tighter pipeline to reduce round trips (Workflow simplification: agent_002 and agent_003)."
+    },
+    {
+      "explanation": "Step 7 — Produce the finalized knowledge graph object with all entities, relations, failures, and optimizations using only line references for raw_prompt_ref and interaction_prompt_ref and empty raw_prompt / interaction_prompt fields as required.",
+      "output": "Final knowledge graph follows."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Python List Comprehension Explanation System",
+    "system_summary": "A user-facing assistant answers a beginner question about Python list comprehensions. The flow: the user query (input_001) is consumed by the main response agent (agent_002). The system uses a documentation search (agent_003) via a Computer Terminal (agent_004) to gather documents; agent_002 synthesizes an explanation and examples (task_001) and produces an explanation output (output_001) delivered to the learner (human_001).",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Python Programming Assistant (System Role)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 28,
+            "line_end": 28
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Response Generator (Main Performer)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 25,
+            "line_end": 31
+          },
+          {
+            "line_start": 38,
+            "line_end": 39
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Documentation Search Agent (Supporting)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 15,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "agent_004",
+        "type": "Tool",
+        "name": "Computer Terminal",
+        "importance": "LOW",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 15,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Explain Python list comprehensions and provide examples",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          },
+          {
+            "line_start": 28,
+            "line_end": 31
+          },
+          {
+            "line_start": 38,
+            "line_end": 39
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User question about Python list comprehensions (initial + follow-up)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          },
+          {
+            "line_start": 38,
+            "line_end": 38
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Explanation and examples of list comprehensions",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 29,
+            "line_end": 29
+          },
+          {
+            "line_start": 39,
+            "line_end": 39
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Learner User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "relation_001",
+        "source": "input_001",
+        "target": "agent_002",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "relation_002",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 29,
+            "line_end": 29
+          },
+          {
+            "line_start": 39,
+            "line_end": 39
+          }
+        ]
+      },
+      {
+        "id": "relation_003",
+        "source": "task_001",
+        "target": "agent_002",
+        "type": "ASSIGNED_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 28,
+            "line_end": 28
+          }
+        ]
+      },
+      {
+        "id": "relation_004",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 29,
+            "line_end": 29
+          },
+          {
+            "line_start": 39,
+            "line_end": 39
+          }
+        ]
+      },
+      {
+        "id": "relation_005",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 29,
+            "line_end": 29
+          },
+          {
+            "line_start": 39,
+            "line_end": 39
+          }
+        ]
+      },
+      {
+        "id": "relation_006",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "INTERVENES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 29,
+            "line_end": 29
+          }
+        ]
+      },
+      {
+        "id": "relation_007",
+        "source": "agent_001",
+        "target": "agent_004",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 17,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "relation_008",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 17,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "relation_009",
+        "source": "agent_004",
+        "target": "task_001",
+        "type": "REQUIRED_BY",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 17,
+            "line_end": 18
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "HALLUCINATION",
+        "description": "Assistant asserted a numeric performance improvement ('20-30% faster') without explicit citation or benchmark in the trace.",
+        "raw_text": "Unreferenced performance claim in assistant output",
+        "raw_text_ref": [
+          {
+            "line_start": 39,
+            "line_end": 39
+          }
+        ],
+        "affected_id": "agent_002"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Response is concise but omits deeper discussion of trade-offs and edge cases (nested comprehensions, readability limits), reducing completeness for learners.",
+        "raw_text": "Brief high-level guidance without extended caveats or detailed nested examples",
+        "raw_text_ref": [
+          {
+            "line_start": 29,
+            "line_end": 29
+          },
+          {
+            "line_start": 39,
+            "line_end": 39
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add an automated benchmarking/citation tool integrated with the Computer Terminal (agent_004) so the assistant can attach measured benchmarks or citations when claiming performance improvements. This reduces hallucinated numeric claims and increases trust.",
+        "affected_ids": [
+          "agent_004",
+          "agent_002",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 17,
+            "line_end": 18
+          },
+          {
+            "line_start": 39,
+            "line_end": 39
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine the assistant's response prompt to require explicit citations for quantitative claims and to include a short 'when not to use' section (readability trade-offs, nested comprehension caveats). This improves completeness and pedagogical quality.",
+        "affected_ids": [
+          "agent_002",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 28,
+            "line_end": 31
+          },
+          {
+            "line_start": 39,
+            "line_end": 39
+          }
+        ]
+      },
+      {
+        "id": "opt_003",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Consolidate retrieval and synthesis into a tighter pipeline (single agent or coordinated microservice) to reduce round trips between search and inference, improving latency and consistency of citations.",
+        "affected_ids": [
+          "agent_002",
+          "agent_003",
+          "agent_004"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 15,
+            "line_end": 18
+          },
+          {
+            "line_start": 25,
+            "line_end": 31
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 7516,
+  "input_trace_preview": "[\n  {\n    \"id\": \"demo_obs_001\",\n    \"type\": \"user_query\",\n    \"timestamp\": \"2025-01-27T00:00:00\",\n    \"input\": \"Hello! I'm learning Python and I keep seeing this syntax with square brackets that looks different from regular loops. Can you help me understand what Python list comprehensions are used for and when I should use them?\",\n    \"output\": \"I'll help you understand Python list comprehensions! Let me search our documentation to give you a comprehensive explanation.\",\n    \"metadata\": {\n      ..."
+}

extraction_analysis/cot_extraction_20250907_161352_ccaa9115.json

@@ -0,0 +1,320 @@
+{
+  "timestamp": "20250907_161352",
+  "extraction_id": "ccaa9115",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Initial trace parsing — identify explicit mentions of agents, tasks, tools, inputs, outputs, and humans from the raw trace lines.",
+      "output": "Found explicit lines: Agent declaration (line 1), Task declaration (line 2), Tool declaration (line 3), and descriptive sentences (lines 5-6) describing processing of customer data to produce business insights for the marketing team."
+    },
+    {
+      "explanation": "Determine main participants and map to canonical entity roles required by the specification. Establish primary workflow elements: input (customer data), processing agent(s), tool (pandas_analyzer), primary task, output (business insights), and human recipient (marketing team).",
+      "output": "Primary participants identified and candidates for IDs: DataAnalyzer (agent), Customer Data (input), Analyze customer data and generate insights (task), pandas_analyzer (tool), Business Insights (output), Marketing Team (human). References: lines 1-3, 5-6."
+    },
+    {
+      "explanation": "Assign entity IDs per rules (agent_001 first-mentioned expert, agent_002 main performer, agent_004 Computer Terminal tool entity representing pandas_analyzer). Populate raw_prompt_ref using line numbers only and set raw_prompt to empty string as required.",
+      "output": "Entities constructed with ids and line refs (see final_answer.entities). Key refs: agent_001 -> line 1 & 5, agent_002 -> line 5, agent_004 -> line 3 & 5, task_001 -> line 2 & 5-6, input_001 -> line 2 & 5, output_001 -> line 6, human_001 -> line 6."
+    },
+    {
+      "explanation": "Map relationships based on explicit sentence saying the DataAnalyzer processes customer data using pandas_analyzer to generate insights for the marketing team. Also enforce the requested critical relationship patterns (input_001 consumed by agent_002, agent_002 performs and is assigned task_001, task produces output_001, output delivered to marketing team) and include tool usage links.",
+      "output": "Relations created (see final_answer.relations). Examples: input_001 --CONSUMED_BY--> agent_002 (ref line 5), agent_002 --PERFORMS--> task_001 (ref line 5), task_001 --PRODUCES--> output_001 (ref line 6), output_001 --DELIVERS_TO--> human_001 (ref line 6), agent_001 --USES--> agent_004 (ref lines 3-5), and agent_004 --REQUIRED_BY--> task_001 (ref lines 3-5). All interaction_prompt fields are empty and references use line numbers only."
+    },
+    {
+      "explanation": "Detect likely failures and risks given the minimal trace: the trace shows processing but no validation, no error handling, and a single tool (pandas_analyzer) that may not scale or handle edge cases. Create explicit failure entries tied to the relevant entities.",
+      "output": "Two inferred failures recorded: missing data validation / potential incorrect insights (EXECUTION_ERROR) affecting agent_002/task_001 (ref lines 5-6); pandas_analyzer scalability or capability limitations (RETRIEVAL_ERROR) affecting agent_004 (ref lines 3 & 5). See final_answer.failures for details and refs."
+    },
+    {
+      "explanation": "Propose optimizations to improve reliability, scalability, and clarity of responsibilities: add validation, enhance or swap tool for big data, consolidate/pre-specify preprocessing.",
+      "output": "Two optimization recommendations: (1) Add explicit data validation and preprocessing step (WORKFLOW_SIMPLIFICATION) affecting task_001 and agent_002 (ref lines 2,5-6). (2) Improve or replace pandas_analyzer for scalability and robustness (TOOL_ENHANCEMENT) affecting agent_004 and agent_002 (ref lines 3 & 5). See final_answer.optimizations."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Customer Data Insights Processing System",
+    "system_summary": "A single DataAnalyzer agent processes customer data using the pandas_analyzer tool to generate business insights for the marketing team. The primary workflow consumes customer data, performs analysis, produces business insights, and delivers them to the marketing team. The Computer Terminal/tool (pandas_analyzer) is required for the task but has potential scalability and validation risks.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataAnalyzer (declared)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Primary Data Processing Agent",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "agent_004",
+        "type": "Tool",
+        "name": "Computer Terminal (pandas_analyzer)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Analyze Customer Data and Generate Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Customer Data",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Business Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Marketing Team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_002",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "agent_002",
+        "type": "ASSIGNED_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "INTERVENES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "agent_001",
+        "target": "agent_004",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "agent_004",
+        "target": "task_001",
+        "type": "REQUIRED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "No explicit data validation or preprocessing is specified; the analyzer may produce incorrect or misleading business insights when input data is dirty or malformed.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ],
+        "affected_id": "task_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "The pandas_analyzer tool may be limited for very large datasets or distributed processing needs; potential performance or capability constraints are not addressed.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "agent_004"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Introduce an explicit data validation and preprocessing step before analysis (either as part of task_001 or a lightweight pre-task) to ensure input quality and reduce incorrect insights.",
+        "affected_ids": [
+          "task_001",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enhance or replace pandas_analyzer for scalability and robustness (e.g., adopt Dask/PySpark or add batching/caching) and add explicit error handling and logging within the tool integration.",
+        "affected_ids": [
+          "agent_004",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 250,
+  "input_trace_preview": "\n    Agent: DataAnalyzer\n    Task: Analyze customer data and generate insights\n    Tool: pandas_analyzer\n    \n    The DataAnalyzer agent processes customer data using pandas_analyzer tool\n    to generate business insights for the marketing team.\n    "
+}

extraction_analysis/cot_extraction_20250907_161742_36aad171.json

@@ -0,0 +1,346 @@
+{
+  "timestamp": "20250907_161742",
+  "extraction_id": "36aad171",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) Read and index the trace lines to create reliable line references for entities and relations.",
+      "output": "Assigned line numbers: 1: 'Agent: DataAnalyzer', 2: 'Task: Analyze customer data and generate insights', 3: 'Tool: pandas_analyzer', 4: blank, 5: 'The DataAnalyzer agent processes customer data using pandas_analyzer tool', 6: 'to generate business insights for the marketing team.'"
+    },
+    {
+      "explanation": "2) Identify main participants (agents, tool, task, inputs/outputs, human) from the trace.",
+      "output": "Detected entities: Agent DataAnalyzer (lines 1,5-6), Task Analyze customer data and generate insights (line 2), Tool pandas_analyzer (lines 3,5), implicit Input customer data (lines 5-6), implicit Output business insights for the marketing team (line 6), Human marketing team (line 6)."
+    },
+    {
+      "explanation": "3) Map entity IDs according to the given ID rules and required IDs. Create the primary task and map the tool to the designated agent_004 slot (Computer Terminal/tool role) as required by the instructions.",
+      "output": "Entities chosen and IDs: agent_001 (DataAnalyzer - first mention), agent_002 (Main processing agent - DataAnalyzer role for required relationships), agent_004 (Tool/Computer Terminal - mapped to pandas_analyzer), task_001 (Analyze customer data and generate insights), input_001 (Customer data), output_001 (Business insights for marketing team), human_001 (Marketing team). All raw_prompt fields set to ''."
+    },
+    {
+      "explanation": "4) Extract explicit runtime interactions and create relations using only line references for evidence and leaving interaction_prompt empty.",
+      "output": "Primary relations (with line references) created from lines 5-6 and line 3 as evidence: input consumption (customer data processed), tool usage (pandas_analyzer), task performance and output generation, and final delivery to the marketing team."
+    },
+    {
+      "explanation": "5) Detect likely failures and risks present or implied by the trace (missing validation and potential tool/performance limits). Provide references to supporting lines.",
+      "output": "Identified failures: (a) No explicit data validation or preprocessing step is described (risk of incorrect results) — evidence lines 2,5-6. (b) pandas_analyzer may not scale to large data volumes and could cause performance or memory issues — evidence lines 3,5."
+    },
+    {
+      "explanation": "6) Propose optimizations to improve robustness, performance, and maintainability based on detected gaps.",
+      "output": "Suggested optimizations: add data validation and preprocessing, scale toolchain with distributed/dataframe engines (Dask/Spark) or batch-processing, provide explicit reporting/delivery step, and consider caching or incremental analytics. References: lines 2-6 (task and processing statement) and line 3 (tool)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Customer Data Analysis and Insights System",
+    "system_summary": "A single-agent system where the DataAnalyzer processes customer data using the pandas_analyzer tool to produce business insights for the marketing team. The workflow consumes customer data, has the DataAnalyzer perform the primary analysis task, and produces insights delivered to the marketing team. The pandas_analyzer tool (mapped to the system Computer Terminal role) is required for execution.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataAnalyzer (First Mention)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Main Processing Agent (DataAnalyzer)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Supporting Expert",
+        "importance": "LOW",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "agent_004",
+        "type": "Tool",
+        "name": "pandas_analyzer (Computer Terminal)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Analyze customer data and generate insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Customer data",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Business insights for the marketing team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Marketing team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_002",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          },
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "agent_002",
+        "type": "ASSIGNED_TO",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "INTERVENES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "agent_001",
+        "target": "agent_004",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          },
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "No explicit data validation or preprocessing step is specified, which risks incorrect or biased insights.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ],
+        "affected_id": "agent_002"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "pandas_analyzer (single-machine tool) may have performance or memory limitations for large customer datasets.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "agent_004"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Introduce an explicit data validation and preprocessing step before analysis to prevent garbage-in/garbage-out. This can be a lightweight automated check in the pipeline.",
+        "affected_ids": [
+          "task_001",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "For larger datasets, replace or augment pandas_analyzer with a scalable engine (Dask, PySpark) or add batching/streaming to avoid memory issues and improve performance.",
+        "affected_ids": [
+          "agent_004",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "opt_003",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Add an explicit delivery/reporting step (automated report generation and versioned insights) to ensure traceability and repeatable delivery to the marketing team.",
+        "affected_ids": [
+          "output_001",
+          "human_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 250,
+  "input_trace_preview": "\n    Agent: DataAnalyzer\n    Task: Analyze customer data and generate insights\n    Tool: pandas_analyzer\n    \n    The DataAnalyzer agent processes customer data using pandas_analyzer tool\n    to generate business insights for the marketing team.\n    "
+}

extraction_analysis/cot_extraction_20250907_162020_cd250aeb.json

@@ -0,0 +1,314 @@
+{
+  "timestamp": "20250907_162020",
+  "extraction_id": "cd250aeb",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Step 1 — Parse trace and establish line mapping. I assigned line numbers to each line of the provided trace to reference content locations in raw_prompt_ref and interaction_prompt_ref.",
+      "output": "Line map used: 1: 'Agent: DataAnalyzer', 2: 'Task: Analyze customer data and generate insights', 3: 'Tool: pandas_analyzer', 4: blank, 5: 'The DataAnalyzer agent processes customer data using pandas_analyzer tool', 6: 'to generate business insights for the marketing team.'"
+    },
+    {
+      "explanation": "Step 2 — Identify primary entities explicitly present in the trace: agent, task, tool, input (implied), output (implied), and human (recipient). For each entity I set raw_prompt empty and referenced relevant line ranges where the entity is mentioned.",
+      "output": "Identified entities: DataAnalyzer (agent), Analyze customer data and generate insights (task), pandas_analyzer (tool), Customer Data (input, implied), Business Insights (output, implied), Marketing Team (human, implied)."
+    },
+    {
+      "explanation": "Step 3 — Apply ID assignment rules and mapping conventions. I assigned entity IDs following the specified patterns (agent_001 first-mentioned agent; agent_004 used for the tool/computational resource). For the primary performer relationship patterns required by the specification, I included agent_002 as the declared main processing agent (the trace contains only one named agent; agent_001 and agent_002 both reference the DataAnalyzer role in the trace).",
+      "output": "Entity ID assignments prepared (agent_001 DataAnalyzer, agent_002 DataAnalyzer (main performer), agent_004 Computer Terminal / pandas_analyzer tool, task_001 main task, input_001 Customer Data, output_001 Business Insights, human_001 Marketing Team)."
+    },
+    {
+      "explanation": "Step 4 — Extract workflow relations based on explicit textual evidence. The sentence at lines 5-6 shows the DataAnalyzer consuming customer data via pandas_analyzer to produce insights for the marketing team. I encoded the required critical relationship patterns (CONSUMED_BY, PERFORMS, ASSIGNED_TO, PRODUCES, DELIVERS_TO, INTERVENES) and included the USES relation between the first-mentioned agent and the tool as requested.",
+      "output": "Relations encoded: input consumed by main agent, main agent performs primary task, primary task assigned to main agent, task produces output, output delivered to human, agent intervenes on the task, and first-mentioned agent uses the tool. All relations reference lines 5-6 (and tool mention at line 3 where relevant)."
+    },
+    {
+      "explanation": "Step 5 — Detect potential failures and risks. The trace includes no explicit errors, so I inferred plausible risks from the minimal description: missing validation & potential tool/scale limitations. I created two failure records with references to the lines that establish the context (tool mention, processing sentence, and output target).",
+      "output": "Failures identified (inferred): missing validation / incomplete verification of insights (EXECUTION_ERROR), and tool limitation / scalability risk for pandas_analyzer (RETRIEVAL_ERROR)."
+    },
+    {
+      "explanation": "Step 6 — Propose optimizations. Based on the workflow and inferred risks, I recommended improvements: enhance or replace the tool for scale, add validation/QA stages, and consider automating pipeline steps to reduce manual oversight. These recommendations reference the same lines describing the processing and the tool.",
+      "output": "Optimizations proposed: tool enhancement or replacement for scalability, add validation/quality assurance step for the generated insights, and consolidate/automate the data processing pipeline."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Customer Data Analysis and Insight Generation System",
+    "system_summary": "A single-agent system where the DataAnalyzer processes customer data using the pandas_analyzer tool to produce business insights delivered to the marketing team. The workflow consumes customer data, performs the primary analysis task, produces insights, and delivers them to the human stakeholder.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataAnalyzer",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "DataAnalyzer (Main Processor)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "agent_004",
+        "type": "Tool",
+        "name": "Computer Terminal (pandas_analyzer)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Analyze Customer Data and Generate Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Customer Data",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Business Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Marketing Team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_002",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "agent_002",
+        "type": "ASSIGNED_TO",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "INTERVENES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "agent_001",
+        "target": "agent_004",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          },
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "No explicit validation or QA step is described — risk of incomplete or unverified insights being produced.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ],
+        "affected_id": "task_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Tool limitation risk: pandas_analyzer is named as the tool but may not scale or handle data-access constraints for large production datasets.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "agent_004"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Replace or augment pandas_analyzer with a scalable data-processing engine or add batching and distributed processing to handle larger datasets and improve reliability.",
+        "affected_ids": [
+          "agent_004",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Add an automated validation/QA step after analysis to verify insights before delivery, or integrate lightweight data-quality checks into the analysis task to reduce risk of incorrect outputs.",
+        "affected_ids": [
+          "task_001",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 250,
+  "input_trace_preview": "\n    Agent: DataAnalyzer\n    Task: Analyze customer data and generate insights\n    Tool: pandas_analyzer\n    \n    The DataAnalyzer agent processes customer data using pandas_analyzer tool\n    to generate business insights for the marketing team.\n    "
+}

extraction_analysis/cot_extraction_20250907_162246_0874b6c0.json

@@ -0,0 +1,312 @@
+{
+  "timestamp": "20250907_162246",
+  "extraction_id": "0874b6c0",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Step 1 — Parse the trace and assign line numbers for reference.",
+      "output": "Trace mapped to lines: 1: Agent: DataAnalyzer; 2: Task: Analyze customer data and generate insights; 3: Tool: pandas_analyzer; 4: (blank); 5: Descriptive sentence mentioning DataAnalyzer processing customer data with pandas_analyzer; 6: Descriptive sentence mentioning generating business insights for the marketing team."
+    },
+    {
+      "explanation": "Step 2 — Identify primary entities (Agent, Task, Tool, Input, Output, Human) and assign IDs following ID rules.",
+      "output": "Entities identified and assigned: agent_001 (DataAnalyzer) refs [1,5]; task_001 (Analyze customer data and generate insights) refs [2,5-6]; tool_001 (pandas_analyzer) refs [3,5]; input_001 (Customer Data) refs [5]; output_001 (Business Insights for Marketing Team) refs [2,6]; human_001 (Marketing Team) refs [6]."
+    },
+    {
+      "explanation": "Step 3 — Map primary workflow relations using explicit mentions in the trace.",
+      "output": "Core relations created: input_001 --CONSUMED_BY--> agent_001 refs [5]; agent_001 --PERFORMS--> task_001 refs [1-2,5]; agent_001 --USES--> tool_001 refs [3,5]; tool_001 --REQUIRED_BY--> task_001 refs [3,5]; task_001 --PRODUCES--> output_001 refs [2,6]; output_001 --DELIVERS_TO--> human_001 refs [6]."
+    },
+    {
+      "explanation": "Step 4 — Classify workflow complexity and ensure task mapping fits guidelines.",
+      "output": "Workflow classification: SIMPLE WORKFLOW (single primary processing agent). Therefore a single main task (task_001) is appropriate and a straightforward linear flow from input to agent to output to human."
+    },
+    {
+      "explanation": "Step 5 — Detect potential failures / risks implied by the trace (tool reliance, missing validation).",
+      "output": "Detected risks: (1) Tool execution/scalability risk for pandas_analyzer (related to tool_001) refs [3,5]. (2) Missing explicit data validation step which may cause incorrect insights (related to task_001 / agent_001) refs [2,5-6]."
+    },
+    {
+      "explanation": "Step 6 — Propose targeted optimizations to improve robustness and scalability.",
+      "output": "Recommendations: (1) Introduce a pre-processing/validation step to clean and validate customer data before analysis (affects task_001, agent_001, input_001) refs [2,5]. (2) Evaluate or upgrade pandas_analyzer for scalability (e.g., Dask/Spark or sampling/streaming) and/or optimize processing (affects tool_001, agent_001) refs [3,5]."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Customer Data Analysis and Insight Generation System",
+    "system_summary": "A simple pipeline where the DataAnalyzer agent consumes customer data, uses the pandas_analyzer tool to execute an analysis task that generates business insights which are delivered to the marketing team. The trace shows one primary agent, one core task, and one tool dependency; key risks include lack of explicit data validation and potential tool scalability limitations.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataAnalyzer",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Analyze Customer Data and Generate Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "pandas analyzer",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Customer Data",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Business Insights for Marketing Team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Marketing Team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "tool_001",
+        "target": "task_001",
+        "type": "REQUIRED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Potential scalability or performance limitation when using the pandas_analyzer tool on large customer datasets.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "tool_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "No explicit data validation/cleaning step is mentioned; dirty or inconsistent customer data may produce incorrect or misleading insights.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Introduce an explicit data validation and cleaning pre-processing step before analysis (e.g., a dedicated Validate and Clean Customer Data task) to reduce incorrect-insight risk and make results more robust.",
+        "affected_ids": [
+          "task_001",
+          "agent_001",
+          "input_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Assess pandas_analyzer scalability and consider switching to or integrating distributed processing (Dask/Spark) or optimize processing (sampling, batching) for large datasets to avoid execution bottlenecks.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 250,
+  "input_trace_preview": "\n    Agent: DataAnalyzer\n    Task: Analyze customer data and generate insights\n    Tool: pandas_analyzer\n    \n    The DataAnalyzer agent processes customer data using pandas_analyzer tool\n    to generate business insights for the marketing team.\n    "
+}

extraction_analysis/cot_extraction_20250907_162459_79012516.json

@@ -0,0 +1,311 @@
+{
+  "timestamp": "20250907_162459",
+  "extraction_id": "79012516",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Workflow complexity analysis: count agents and classify workflow type. According to the trace there is one distinct agent (DataAnalyzer). With 1 agent the workflow is SIMPLE (1-2 agents) and should use a single consolidated task.",
+      "output": "COUNT_AGENTS: 1; WORKFLOW_CLASSIFICATION: SIMPLE (single consolidated task). Task decomposition: single primary task assigned to the DataAnalyzer. No sequential multi-task NEXT relations required."
+    },
+    {
+      "explanation": "Entity extraction: identify primary participants from the trace (agent, task, tool, implicit input, implicit output, human stakeholder). Map each to an entity id and locate the lines where they are mentioned.",
+      "output": "Entities identified: agent_001 Data Analyzer (lines 1-1), task_001 Analyze customer data and generate insights (lines 2-2), tool_001 pandas analyzer (lines 3-3), input_001 Customer Data (implicit from task and sentence, lines 2-2 and 5-5), output_001 Business Insights (implicit from sentence, lines 5-6), human_001 Marketing Team (lines 6-6)."
+    },
+    {
+      "explanation": "Relation mapping for a SIMPLE workflow: connect input → agent, agent → performs task, agent → uses tool, task → produces output, output → delivered to human. Also mark the tool as required by the task since the trace states the agent processes data using the tool.",
+      "output": "Relations created: rel_001 input_001 → agent_001 (CONSUMED_BY, lines 2 and 5), rel_002 agent_001 → task_001 (PERFORMS, lines 1-2 and 5), rel_003 agent_001 → tool_001 (USES, lines 3 and 5), rel_004 tool_001 → task_001 (REQUIRED_BY, lines 3 and 5), rel_005 task_001 → output_001 (PRODUCES, lines 5-6), rel_006 output_001 → human_001 (DELIVERS_TO, line 6)."
+    },
+    {
+      "explanation": "Failure and risk detection: the trace contains no explicit error messages, but the minimal workflow exposes two risks: missing data validation/QA step and tool scalability/robustness dependency. These are inferred from the content describing processing and tool usage without validation or fallback.",
+      "output": "failure_001: Missing data validation and verification step (PLANNING_ERROR) affecting task_001 — evidence: lines 2 and 5. failure_002: Tool dependency / potential execution limitation with pandas_analyzer (EXECUTION_ERROR) affecting tool_001 — evidence: lines 3 and 5."
+    },
+    {
+      "explanation": "Optimization suggestions: propose concrete improvements to address detected risks and improve efficiency. Each recommendation ties to affected entities and references trace locations that motivated the suggestion.",
+      "output": "opt_001: Add explicit data validation and QA step (WORKFLOW_SIMPLIFICATION) to prevent incorrect insights; affect task_001 and agent_001; evidence lines 2 and 5. opt_002: Enhance or replace pandas_analyzer with scalable processing (e.g., distributed engine or batching) and add monitoring (TOOL_ENHANCEMENT); affect tool_001 and agent_001; evidence lines 3 and 5."
+    },
+    {
+      "explanation": "Assemble final knowledge graph structure (SIMPLE workflow) following required schema constraints: raw_prompt empty for all entities, interaction_prompt empty for relations, use line references only.",
+      "output": "Final knowledge graph assembled with entities, relations, failures and optimizations mapped to line references."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Customer Data Analysis and Insights System",
+    "system_summary": "A single-agent system where the Data Analyzer processes customer data using the pandas_analyzer tool to generate business insights delivered to the marketing team. The workflow is simple: the Data Analyzer consumes customer data, performs analysis, produces business insights, and those insights are delivered to the marketing team. The pandas_analyzer tool is required for the task.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Data Analyzer",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Analyze Customer Data and Generate Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "pandas analyzer",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Customer Data",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Business Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Marketing Team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "tool_001",
+        "target": "task_001",
+        "type": "REQUIRED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "PLANNING_ERROR",
+        "description": "No explicit data validation or QA step is described; analytics outputs could be incorrect or unverified.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "task_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Heavy reliance on pandas_analyzer may cause execution or scalability issues for large datasets.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "tool_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Insert an explicit data validation and QA step (pre- and post-analysis) to ensure correctness of generated insights and reduce downstream risk.",
+        "affected_ids": [
+          "task_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Improve tool robustness by adopting scalable data processing (e.g., distributed engines, batching, or replacing pandas_analyzer with a distributed alternative) and add monitoring to detect execution issues early.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 250,
+  "input_trace_preview": "\n    Agent: DataAnalyzer\n    Task: Analyze customer data and generate insights\n    Tool: pandas_analyzer\n    \n    The DataAnalyzer agent processes customer data using pandas_analyzer tool\n    to generate business insights for the marketing team.\n    "
+}

extraction_analysis/cot_extraction_20250907_162635_844d2fd2.json

@@ -0,0 +1,288 @@
+{
+  "timestamp": "20250907_162635",
+  "extraction_id": "844d2fd2",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Workflow complexity analysis: count agents and classify workflow. Identify whether to use a single consolidated task or decompose into multiple specialized tasks.",
+      "output": "COUNT AGENTS: 1 distinct agent (DataAnalyzer). CLASSIFY WORKFLOW: SIMPLE (1-2 agents) -> single consolidated task. TASK DECOMPOSITION: Single primary task assigned to the DataAnalyzer (no multi-agent decomposition required). SEQUENTIAL FLOW: Not applicable for a simple workflow (no NEXT relations required)."
+    },
+    {
+      "explanation": "Extract main entities (agents, task, tool, input, output, human) from the trace and assign IDs and references. Ensure raw_prompt is empty and raw_prompt_ref uses line numbers where each entity is mentioned.",
+      "output": "Entities extracted:\n- agent_001: Data Analyzer (HIGH) referenced at lines 1 and 5\n- task_001: Analyze Customer Data and Generate Insights (HIGH) referenced at lines 2 and 5-6\n- tool_001: pandas analyzer (MEDIUM) referenced at lines 3 and 5\n- input_001: Customer Data (HIGH) referenced at line 5\n- output_001: Business Insights (HIGH) referenced at lines 5-6\n- human_001: Marketing Team (HIGH) referenced at line 6"
+    },
+    {
+      "explanation": "Map relationships between extracted entities according to SIMPLE workflow pattern: CONSUMED_BY, PERFORMS, USES, PRODUCES, DELIVERS_TO. Use interaction_prompt empty and interaction_prompt_ref with line numbers evidencing each relationship.",
+      "output": "Relations extracted:\n- rel_001: input_001 --CONSUMED_BY--> agent_001 (evidence line 5)\n- rel_002: agent_001 --PERFORMS--> task_001 (evidence line 5)\n- rel_003: agent_001 --USES--> tool_001 (evidence line 5)\n- rel_004: task_001 --PRODUCES--> output_001 (evidence lines 5-6)\n- rel_005: output_001 --DELIVERS_TO--> human_001 (evidence line 6)\n- rel_006: tool_001 --REQUIRED_BY--> task_001 (evidence line 5) (tool is an essential dependency for the task)"
+    },
+    {
+      "explanation": "Detect potential failures and risks based on trace content and absence of explicit validation or redundancy. Provide at least 1-2 failure items with references to trace lines that motivate the risk assessment.",
+      "output": "Failures detected:\n- failure_001: Risk of single-point-of-failure or tool limitation because the workflow depends solely on pandas_analyzer (risk_type: EXECUTION_ERROR / TOOL_DEPENDENCY). raw_text_ref: line 3 and line 5. affected_id: tool_001\n- failure_002: Lack of explicit validation or human review may lead to incorrect or unverified insights produced by the Data Analyzer (risk_type: HALLUCINATION / EXECUTION_ERROR). raw_text_ref: lines 2 and 5-6. affected_id: agent_001"
+    },
+    {
+      "explanation": "Suggest optimizations to improve reliability, validation, and scalability. Provide 2 optimization recommendations with affected entity ids and line references.",
+      "output": "Optimizations:\n- opt_001 (TOOL_ENHANCEMENT): Add redundancy and fallback analysis tools or wrap pandas_analyzer calls with robust error handling and input validation to avoid single-point failures. Affected ids: [tool_001, agent_001]. raw_text_ref: lines 3 and 5.\n- opt_002 (WORKFLOW_SIMPLIFICATION): Introduce an automated validation step or lightweight human-in-the-loop review after insights are generated (a short validation/QA task) to catch incorrect results before delivery. Affected ids: [task_001, human_001, agent_001]. raw_text_ref: lines 2, 5-6."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Customer Data Insight Generation System",
+    "system_summary": "A simple system where the Data Analyzer agent consumes customer data, uses the pandas analyzer tool to analyze it, and produces business insights delivered to the marketing team. The workflow is a single-step processing pipeline with an explicit tool dependency and direct delivery to stakeholders.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Data Analyzer",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Analyze Customer Data and Generate Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "pandas analyzer",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Customer Data",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Business Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Marketing Team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "tool_001",
+        "target": "task_001",
+        "type": "REQUIRED_BY",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Single tool dependency (pandas analyzer) creates a single-point-of-failure or limitation risk for analysis execution.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "tool_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "HALLUCINATION",
+        "description": "No explicit validation step or human review is present; generated insights may be incorrect or unverified.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ],
+        "affected_id": "agent_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Introduce fallback or redundant analysis tools and robust error handling around pandas analyzer; add automated input validation prior to analysis to reduce tool failures.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Add a lightweight validation or human-in-the-loop QA step after insight generation to verify outputs before delivery to the marketing team.",
+        "affected_ids": [
+          "task_001",
+          "human_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 250,
+  "input_trace_preview": "\n    Agent: DataAnalyzer\n    Task: Analyze customer data and generate insights\n    Tool: pandas_analyzer\n    \n    The DataAnalyzer agent processes customer data using pandas_analyzer tool\n    to generate business insights for the marketing team.\n    "
+}

extraction_analysis/cot_extraction_20250907_162741_8d3e9c1b.json

@@ -0,0 +1,268 @@
+{
+  "timestamp": "20250907_162741",
+  "extraction_id": "8d3e9c1b",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Workflow complexity analysis: count agents and classify workflow. According to the trace there is a single named agent 'DataAnalyzer'. Apply complexity rules: 1 agent → SIMPLE workflow (single consolidated task).",
+      "output": "AGENTS_COUNT: 1; WORKFLOW_CLASSIFICATION: SIMPLE (use single consolidated task)."
+    },
+    {
+      "explanation": "Extract main entities (agents, task, tool, input, output, human) from the trace and assign IDs. All raw_prompt fields MUST be empty strings; provide line references where each entity is mentioned.",
+      "output": "Entities identified and mapped to IDs: agent_001 DataAnalyzer (HIGH) refs lines 1 and 5; task_001 Customer Data Analysis and Insight Generation (HIGH) refs lines 2 and 5; tool_001 pandas analyzer (MEDIUM) refs lines 3 and 5; input_001 Customer Data (HIGH) refs lines 2 and 5; output_001 Business Insights (HIGH) refs lines 5-6; human_001 Marketing Team (HIGH) refs line 6."
+    },
+    {
+      "explanation": "Map primary relationships for a SIMPLE workflow: INPUT consumed by AGENT; AGENT performs TASK; TASK produces OUTPUT; OUTPUT delivered to HUMAN; AGENT uses TOOL. Each relation gets an interaction_prompt_ref referencing trace lines showing the interaction.",
+      "output": "Relations established: rel_001 input_001 --CONSUMED_BY--> agent_001 (HIGH) refs lines 2-5; rel_002 agent_001 --PERFORMS--> task_001 (HIGH) refs lines 1-2 and 5; rel_003 task_001 --PRODUCES--> output_001 (HIGH) refs lines 5-6; rel_004 output_001 --DELIVERS_TO--> human_001 (HIGH) ref line 6; rel_005 agent_001 --USES--> tool_001 (MEDIUM) refs lines 3 and 5."
+    },
+    {
+      "explanation": "Detect possible failures or risks suggested by the trace (no explicit errors shown). Provide at least two inferred risks referencing relevant lines.",
+      "output": "Inferred failures: failure_001 EXECUTION_ERROR - risk that pandas_analyzer processing may fail or raise exceptions during analysis (refs lines 3-5) affected agent_001; failure_002 RETRIEVAL_ERROR - risk of incomplete or missing customer data (refs lines 2 and 5) affecting input_001/tool_001."
+    },
+    {
+      "explanation": "Propose optimizations to improve reliability and efficiency based on the small workflow (tool enhancements and workflow simplification). Provide references to trace lines motivating the suggestions.",
+      "output": "Optimizations: opt_001 TOOL_ENHANCEMENT - add robust data validation, error handling, and performance tuning for pandas_analyzer (refs lines 3-5) affected_ids [tool_001]; opt_002 WORKFLOW_SIMPLIFICATION - automate ingestion + preprocessing steps and integrate lightweight reporting so DataAnalyzer directly outputs marketing-ready insights (refs lines 2-6) affected_ids [agent_001, task_001]."
+    },
+    {
+      "explanation": "Assemble final knowledge graph object with entities, relations, failures, and optimizations. All raw_prompt fields are empty strings and all interaction_prompt fields are empty strings; only line number references are used.",
+      "output": "Final knowledge graph assembled (see final_answer)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Customer Data Insights Generation System",
+    "system_summary": "A simple single-agent pipeline where the DataAnalyzer consumes customer data, uses the pandas analyzer tool to analyze and generate business insights, and delivers those insights to the marketing team. The workflow is single-step (simple) with the agent performing a consolidated analysis and insight generation task.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataAnalyzer",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Customer Data Analysis and Insight Generation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "pandas analyzer",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Customer Data",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Business Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Marketing Team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Analysis execution may fail due to runtime errors or exceptions in pandas analyzer during data processing.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Customer data may be incomplete or unavailable, causing insufficient or biased insights.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "input_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Harden pandas analyzer usage: add input validation, robust exception handling, and performance optimizations (vectorized ops, memory management).",
+        "affected_ids": [
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Automate ingestion and lightweight preprocessing inside the same consolidated task so DataAnalyzer outputs marketing-ready reports with fewer manual steps.",
+        "affected_ids": [
+          "agent_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 6
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 250,
+  "input_trace_preview": "\n    Agent: DataAnalyzer\n    Task: Analyze customer data and generate insights\n    Tool: pandas_analyzer\n    \n    The DataAnalyzer agent processes customer data using pandas_analyzer tool\n    to generate business insights for the marketing team.\n    "
+}

extraction_analysis/cot_extraction_20250907_162848_3f13d986.json

@@ -0,0 +1,288 @@
+{
+  "timestamp": "20250907_162848",
+  "extraction_id": "3f13d986",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Workflow complexity analysis: count agents and classify workflow type, then determine task decomposition and sequential flow requirements per rules.",
+      "output": "COUNT AGENTS: 1 distinct agent (DataAnalyzer). CLASSIFY WORKFLOW: SIMPLE (1-2 agents). TASK DECOMPOSITION: single consolidated task (no decomposition into multiple specialized tasks). SEQUENTIAL FLOW: none required (no NEXT relations)."
+    },
+    {
+      "explanation": "Extract main entities from the trace: agent(s), task(s), tool(s), input(s), output(s), human(s). Map each entity to an ID and record line references where they appear.",
+      "output": "Entities identified and mapped to IDs: agent_001 (DataAnalyzer) referenced line 1; task_001 (Analyze customer data and generate insights) referenced line 2; tool_001 (pandas analyzer) referenced line 3; input_001 (Customer Data) referenced line 5; output_001 (Business Insights) referenced lines 2 and 5-6; human_001 (Marketing Team) referenced line 6."
+    },
+    {
+      "explanation": "Extract relationships following SIMPLE workflow pattern: Input → Agent → Task → Output → Human. Also identify tool usage / required-by relationships evidenced in the trace.",
+      "output": "Relationships extracted: input_001 CONSUMED_BY agent_001 (evidence line 5). agent_001 PERFORMS task_001 (evidence lines 1-2). task_001 PRODUCES output_001 (evidence lines 2,5-6). output_001 DELIVERS_TO human_001 (evidence line 6). agent_001 USES tool_001 (evidence line 5). tool_001 REQUIRED_BY task_001 (evidence lines 2-3,5)."
+    },
+    {
+      "explanation": "Detect possible failures/risks in the workflow based on trace content and typical risks for this pattern (tool dependency, data quality, lack of validation). Provide at least one or two identified risks with supporting line references.",
+      "output": "Failures/risks: (1) Tool limitation / compatibility risk due to reliance on pandas_analyzer (risk_type: RETRIEVAL_ERROR) referenced lines 3 and 5. (2) Potential incorrect or unvalidated insights due to no explicit validation or preprocessing step (risk_type: EXECUTION_ERROR / HALLUCINATION) referenced lines 2 and 5."
+    },
+    {
+      "explanation": "Suggest optimizations to improve reliability, validation, and maintainability of the workflow, with references to trace lines motivating each suggestion.",
+      "output": "Optimizations: add explicit data validation and preprocessing stage (references lines 2,5); add error handling and fallback analysis tools or wrappers around pandas_analyzer (references lines 3,5); consider adding logging and result verification before delivering to the marketing team (references lines 5-6)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Customer Data Insights Generation System",
+    "system_summary": "A simple single-agent system where the DataAnalyzer processes customer data using a pandas-based analysis tool to produce business insights for the marketing team. The workflow consumes customer data, is executed by the DataAnalyzer performing a consolidated analysis task that relies on the pandas analyzer tool, and delivers insights to the marketing team.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataAnalyzer",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Analyze Customer Data and Generate Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "pandas analyzer",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Customer Data",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Business Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Marketing Team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "tool_001",
+        "target": "task_001",
+        "type": "REQUIRED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 5
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Dependency on the pandas analyzer tool creates a single-point-of-failure or compatibility risk if the tool is unavailable or limited.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "tool_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "No explicit data validation or preprocessing is mentioned, risking incorrect or unvalidated insights being produced.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Wrap pandas analyzer calls with robust error handling and provide fallback analysis methods or retry logic to reduce single-tool failure risk.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Introduce a lightweight data validation/preprocessing step before analysis to ensure input quality and reduce erroneous outputs delivered to the marketing team.",
+        "affected_ids": [
+          "task_001",
+          "input_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 5
+          },
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 250,
+  "input_trace_preview": "\n    Agent: DataAnalyzer\n    Task: Analyze customer data and generate insights\n    Tool: pandas_analyzer\n    \n    The DataAnalyzer agent processes customer data using pandas_analyzer tool\n    to generate business insights for the marketing team.\n    "
+}

extraction_analysis/cot_extraction_20250907_164651_7614388b.json

@@ -0,0 +1,285 @@
+{
+  "timestamp": "20250907_164651",
+  "extraction_id": "7614388b",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "STEP 1 - STRUCTURAL JSON PARSING (ontology-enhanced). Locate structural elements, extract agent(s), tools, inputs and outputs from the trace and map to line references.",
+      "output": "Found plain-text trace (no explicit JSON keys). Mapped lines for parsing: line 1..6. Extracted: Agent: DataAnalyzer (lines 1,5); Task description (lines 2,5-6); Tool: pandas_analyzer (lines 3,5); Input: customer data (lines 2,5); Output: business insights for marketing team (lines 5-6)."
+    },
+    {
+      "explanation": "STEP 2 - DOMAIN-AWARE AGENT ANALYSIS. Count distinct agents, infer domain expertise and capabilities, and note naming patterns.",
+      "output": "Distinct agents count: 1. Agent identity: DataAnalyzer (treated as Data Analysis expert). Naming pattern: single token 'DataAnalyzer' (no '_Expert' suffix). Capabilities inferred: data processing, feature extraction, statistical summarization and generating marketing insights (based on task description). Relevant lines: agent mention line 1; activity mention lines 5-6."
+    },
+    {
+      "explanation": "STEP 3 - WORKFLOW COMPLEXITY CLASSIFICATION. Use agent count to classify workflow complexity per rules.",
+      "output": "Agent count = 1 → SIMPLE workflow (1 agent). Use single consolidated task pattern. Relevant lines: 1-6."
+    },
+    {
+      "explanation": "STEP 4 - ONTOLOGY-COMPLIANT TASK DECOMPOSITION (simple workflow). Create a single task that the single agent performs and identify input/output/human.",
+      "output": "Decomposed into: agent_001 (Data Analyzer) performs task_001 (Customer Data Analysis and Insights Generation). Input: input_001 (Customer Data). Output: output_001 (Business Insights for Marketing Team). Human stakeholder: human_001 (Marketing Team). Tool used: tool_001 (Pandas Analyzer). Line refs: task def line 2 and lines 5-6; tool line 3 and 5; input lines 2 and 5; output lines 5-6; human line 6."
+    },
+    {
+      "explanation": "STEP 5 - UNIFIED ENTITY-RELATION EXTRACTION. Generate entities and relations ensuring all relations reference existing entity IDs. Apply importance prioritization.",
+      "output": "Entities created: agent_001 (HIGH), task_001 (HIGH), tool_001 (MEDIUM), input_001 (HIGH), output_001 (HIGH), human_001 (HIGH). Relations created: rel_001 input_001→agent_001 CONSUMED_BY (HIGH); rel_002 agent_001→task_001 PERFORMS (HIGH); rel_003 agent_001→tool_001 USES (MEDIUM); rel_004 task_001→output_001 PRODUCES (HIGH); rel_005 output_001→human_001 DELIVERS_TO (HIGH). All relations reference lines 5 and 6 as runtime evidence."
+    },
+    {
+      "explanation": "STEP 6 - FAILURE DETECTION. Look for explicit failures or implicit risks in the trace and produce failure records with justification and line references.",
+      "output": "No explicit runtime errors present in trace. Two potential risks identified: failure_001 - missing validation/verification step for analysis outputs (planning/design risk) referenced by the task description (line 2) and processing statement (line 5). failure_002 - single-agent single-point-of-failure and limited tool dependency risk referenced by agent mention (line 1) and tool mention (line 3). Both are flagged as risks (PLANNING_ERROR and AGENT_ERROR respectively)."
+    },
+    {
+      "explanation": "STEP 7 - OPTIMIZATIONS. Provide actionable recommendations to improve robustness and efficiency based on the trace.",
+      "output": "opt_001: Add an automated validation/verification stage (Data Verification or QA checks) after analysis to ensure result quality. Refs: lines 2,5-6. opt_002: Enhance or extend the pandas_analyzer tool with built-in validation modules and failure-handling (or add a lightweight computing fallback) to reduce single-tool dependency. Refs: lines 3,5."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Customer Data Analysis and Insights Generation System",
+    "system_summary": "A single-agent system where a Data Analyzer processes customer data using a pandas-based analysis tool to generate business insights for the marketing team. The workflow is simple: customer data is consumed by the Data Analyzer, which performs a consolidated analysis task leveraging the pandas analyzer tool and produces insights delivered to the marketing team.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Data Analyzer",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Customer Data Analysis and Insights Generation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Pandas Analyzer",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Customer Data",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Business Insights for Marketing Team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Marketing Team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "PLANNING_ERROR",
+        "description": "No validation/verification step is defined—risk of delivering unverified or inaccurate insights.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ],
+        "affected_id": "task_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "AGENT_ERROR",
+        "description": "Single-agent setup and direct dependency on a single tool create a single point of failure and potential tool-limitation risks.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ],
+        "affected_id": "agent_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Introduce an automated validation step or lightweight Data Verification role (or add validation subtask) after analysis to verify results before delivery.",
+        "affected_ids": [
+          "task_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enhance pandas_analyzer with built-in validation checks, error handling, and optional fallback data-processing routines to reduce single-tool dependency and improve robustness.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 250,
+  "input_trace_preview": "\n    Agent: DataAnalyzer\n    Task: Analyze customer data and generate insights\n    Tool: pandas_analyzer\n    \n    The DataAnalyzer agent processes customer data using pandas_analyzer tool\n    to generate business insights for the marketing team.\n    "
+}

extraction_analysis/cot_extraction_20250907_164859_e082a49b.json

@@ -0,0 +1,290 @@
+{
+  "timestamp": "20250907_164859",
+  "extraction_id": "e082a49b",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "STEP 1 - STRUCTURAL JSON PARSING (ontology-enhanced): locate structural elements, extract agents, tools, and I/O from the trace text.",
+      "output": "Findings:\n- Trace lines (referenced below) contain explicit short-form definitions rather than a JSON object. I map line numbers as:\n  1: Agent: DataAnalyzer\n  2: Task: Analyze customer data and generate insights\n  3: Tool: pandas_analyzer\n  4: (blank)\n  5: The DataAnalyzer agent processes customer data using pandas_analyzer tool\n  6: to generate business insights for the marketing team.\nExtracted elements:\n- Agents: DataAnalyzer (lines 1,5)\n- Tools: pandas analyzer (pandas_analyzer) (lines 3,5)\n- Task (explicit): Analyze customer data and generate insights (lines 2,5-6)\n- Input (implicit): Customer data (in task text, lines 2,5)\n- Output (implicit): Business insights for the marketing team (lines 5-6)"
+    },
+    {
+      "explanation": "STEP 2 - DOMAIN-AWARE AGENT ANALYSIS: count distinct agents, infer role/expertise, naming patterns, and capabilities.",
+      "output": "Findings:\n- Distinct agents counted from the trace: 1 (DataAnalyzer) — SIMPLE workflow per rules.\n- Role/expertise: DataAnalyzer represents a data analysis specialist able to process customer data and produce business insights.\n- Naming pattern: No _Expert/_Agent suffix; plain name 'DataAnalyzer' (lines 1,5).\n- Capabilities inferred: data ingestion/preprocessing, analytics using pandas, summarization/insight generation, delivery of results to marketing (evidence lines 5-6)."
+    },
+    {
+      "explanation": "STEP 3 - WORKFLOW COMPLEXITY CLASSIFICATION: determine simple vs complex using agent count.",
+      "output": "Findings:\n- Agent count = 1 → CLASSIFICATION: SIMPLE WORKFLOW (1 agent).\n- Consequence: use single consolidated task (task_001) rather than decomposing into 3 specialized tasks."
+    },
+    {
+      "explanation": "STEP 4 - ONTOLOGY-COMPLIANT TASK DECOMPOSITION (for SIMPLE workflow): define primary task, inputs, outputs and map to agent.",
+      "output": "Generated primary task and I/O mapping:\n- task_001: Analyze Customer Data and Generate Insights (lines 2,5-6)\n- input_001: Customer Data (lines 2,5)\n- output_001: Business Insights (for marketing team) (lines 5-6)\n- agent_001 (DataAnalyzer) performs task_001 and consumes input_001; uses tool pandas analyzer."
+    },
+    {
+      "explanation": "STEP 5 - UNIFIED ENTITY-RELATION EXTRACTION: create entities and relations with consistent IDs and line references (raw_prompt empty per requirements; relations have empty interaction_prompt).",
+      "output": "Entities (summary with line refs):\n- agent_001: DataAnalyzer (Agent), importance HIGH, raw_prompt_ref [{line_start:1,line_end:1},{line_start:5,line_end:5}]\n- task_001: Analyze Customer Data and Generate Insights (Task), importance HIGH, raw_prompt_ref [{line_start:2,line_end:2},{line_start:5,line_end:6}]\n- tool_001: pandas analyzer (Tool), importance MEDIUM, raw_prompt_ref [{line_start:3,line_end:3},{line_start:5,line_end:5}]\n- input_001: Customer Data (Input), importance HIGH, raw_prompt_ref [{line_start:2,line_end:2},{line_start:5,line_end:5}]\n- output_001: Business Insights (Output), importance HIGH, raw_prompt_ref [{line_start:5,line_end:5},{line_start:6,line_end:6}]\n- human_001: Marketing Team (Human), importance HIGH, raw_prompt_ref [{line_start:6,line_end:6}]\n\nRelations (summary with line refs):\n- rel_001: input_001 --CONSUMED_BY--> agent_001, importance HIGH, interaction_prompt_ref [{line_start:5,line_end:5}]\n- rel_002: agent_001 --PERFORMS--> task_001, importance HIGH, interaction_prompt_ref [{line_start:1,line_end:2},{line_start:5,line_end:5}]\n- rel_003: agent_001 --USES--> tool_001, importance MEDIUM, interaction_prompt_ref [{line_start:5,line_end:5}]\n- rel_004: task_001 --PRODUCES--> output_001, importance HIGH, interaction_prompt_ref [{line_start:5,line_end:6}]\n- rel_005: output_001 --DELIVERS_TO--> human_001, importance HIGH, interaction_prompt_ref [{line_start:6,line_end:6}]\n\n(These entities/relations are produced to match the SIMPLE workflow template from the spec.)"
+    },
+    {
+      "explanation": "STEP 6 - FAILURE DETECTION and OPTIMIZATIONS: identify likely risks and suggest improvements based on the trace and missing components.",
+      "output": "Failures (inferred from trace evidence):\n- failure_001: EXECUTION_ERROR — \"No explicit data validation or quality checks are present; outputs may be incorrect or biased.\" raw_text_ref [{line_start:2,line_end:2},{line_start:5,line_end:5}] affected_id: agent_001\n- failure_002: PLANNING_ERROR — \"No scalability or tool-selection considerations; pandas may not scale for large customer datasets.\" raw_text_ref [{line_start:3,line_end:3},{line_start:5,line_end:5}] affected_id: tool_001\n\nOptimizations (recommended):\n- opt_001 (TOOL_ENHANCEMENT): \"Introduce automated data validation and profiling (e.g., Great Expectations) and consider scalable processing (Dask/Spark) for large datasets; complement pandas where necessary.\" affected_ids: [\"tool_001\",\"agent_001\",\"task_001\"] raw_text_ref [{line_start:2,line_end:2},{line_start:3,line_end:3},{line_start:5,line_end:5}]\n- opt_002 (WORKFLOW_SIMPLIFICATION): \"Add a lightweight human-in-the-loop review of generated insights before final delivery to the marketing team to ensure relevance and interpretability.\" affected_ids: [\"human_001\",\"task_001\"] raw_text_ref [{line_start:5,line_end:6}]"
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Customer Data Insights Generation System",
+    "system_summary": "A simple single-agent system where a DataAnalyzer ingests customer data, processes it using the pandas analyzer tool to produce business insights, and delivers those insights to the marketing team. The workflow is a single-step analysis (ingest→analyze→deliver) and currently lacks explicit validation and scalability provisions.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataAnalyzer",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Analyze Customer Data and Generate Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "pandas analyzer",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Customer Data",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Business Insights",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          },
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Marketing Team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "No explicit data validation or quality checks are present; outputs may be incorrect or biased.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "No scalability considerations or alternative tooling noted; pandas analyzer may be insufficient for large datasets.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "tool_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Introduce automated data validation and profiling (for example, Great Expectations) and consider scalable processing alternatives (Dask, Spark) to complement pandas for larger datasets.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Add a lightweight human-in-the-loop review step for the marketing team to validate and interpret insights before full delivery, improving trust and relevance.",
+        "affected_ids": [
+          "human_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 250,
+  "input_trace_preview": "\n    Agent: DataAnalyzer\n    Task: Analyze customer data and generate insights\n    Tool: pandas_analyzer\n    \n    The DataAnalyzer agent processes customer data using pandas_analyzer tool\n    to generate business insights for the marketing team.\n    "
+}

extraction_analysis/cot_extraction_20250907_165028_42fae05d.json

@@ -0,0 +1,254 @@
+{
+  "timestamp": "20250907_165028",
+  "extraction_id": "42fae05d",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "STEP 1 - STRUCTURAL JSON PARSING (ontology-enhanced). Locate structural cues and extract agent/tool/input/output mentions with line refs.",
+      "output": "Found no embedded JSON object. Detected explicit labelled lines: Agent (line 1), Task (line 2), Tool (line 3). Execution sentence spans lines 5-6 and references customer data, pandas_analyzer, and marketing team. Parsed elements: agents = [DataAnalyzer], tools = [pandas_analyzer], task label = [Analyze customer data and generate insights], input inferred = [customer data], output inferred = [business insights for the marketing team]. (Line map used: 1..6)"
+    },
+    {
+      "explanation": "STEP 2 - DOMAIN-AWARE AGENT ANALYSIS. Count unique agents, infer role/expertise, naming patterns and capabilities.",
+      "output": "Distinct agent count = 1. Agent name: DataAnalyzer (line 1). Role inferred: Data analysis specialist (Data Analysis Expert). Naming pattern: no _Expert suffix but domain-specific name 'DataAnalyzer'. Capabilities inferred from trace: processes customer data, generates business insights, uses pandas_analyzer (evidence lines 1,2,3,5)."
+    },
+    {
+      "explanation": "STEP 3 - WORKFLOW COMPLEXITY CLASSIFICATION. Determine SIMPLE vs COMPLEX using agent count from parsing.",
+      "output": "Using agent count = 1 -> Classified as SIMPLE workflow (1 agent). Therefore apply simple-workflow decomposition: single consolidated task performed by the single agent, direct input consumption and output production (per requirements)."
+    },
+    {
+      "explanation": "STEP 4 - ONTOLOGY-COMPLIANT TASK DECOMPOSITION (for SIMPLE workflow). Create consolidated task mapping.",
+      "output": "Single task created: Customer data analysis and insights generation (maps to the labelled Task line 2). Input: Customer data (inferred from line 5). Output: Business insights for the marketing team (inferred from lines 5-6)."
+    },
+    {
+      "explanation": "STEP 5 - UNIFIED ENTITY-RELATION EXTRACTION. Generate entities (agent, task, tool, input, output, human) and relations consistent with IDs and references. raw_prompt values are empty strings per rules; all references use line numbers from the trace.",
+      "output": "Entities and relations prepared. Entities: agent_001 (DataAnalyzer), task_001 (Customer Data Analysis and Insights Generation), tool_001 (pandas analyzer), input_001 (Customer Data), output_001 (Business Insights for Marketing), human_001 (Marketing Team). Relations (SIMPLE workflow pattern): input_001 --CONSUMED_BY--> agent_001; agent_001 --PERFORMS--> task_001; task_001 --PRODUCES--> output_001; output_001 --DELIVERS_TO--> human_001; agent_001 --USES--> tool_001. All relation refs point to execution/label lines (lines 1-6)."
+    },
+    {
+      "explanation": "STEP 6 - FAILURE DETECTION and OPTIMIZATIONS. Identify potential risks (based on trace omissions or tool choice) and suggest improvements with justification and refs.",
+      "output": "Detected potential failures: (1) Missing explicit validation/verification step for analysis results (planning gap) — evidence: task description and execution lines 2 and 5 show analysis and results but no validation mention (lines 2,5). (2) Tool limitation risk: pandas_analyzer referenced but no scalability/limits discussed — evidence: tool mention line 3. Optimizations: (opt_001) Add an explicit validation step or Data Verification agent (WORKFLOW_SIMPLIFICATION / AGENT_ADDITION) to ensure insights quality (ref lines 2,5). (opt_002) Hardening the pandas_analyzer usage with tooling enhancements (memory/scale checks or alternative engines) to improve reliability for large datasets (TOOL_ENHANCEMENT) (ref line 3)."
+    },
+    {
+      "explanation": "STEP 7 - FINALIZATION. Provide the unified Knowledge Graph structure (entities, relations, failures, optimizations, system summary). All entity raw_prompt fields set to empty string and all relation interaction_prompt fields set to empty string per formatting rules.",
+      "output": "Knowledge graph assembled in final_answer below."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Customer Data Analysis and Insights System",
+    "system_summary": "A single-agent data analysis system where the DataAnalyzer consumes customer data, processes it using the pandas_analyzer tool to generate business insights, and delivers those insights to the marketing team. The workflow is simple: input consumption → single analysis task → output produced for marketing.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataAnalyzer",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Customer Data Analysis and Insights Generation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "pandas analyzer",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Customer Data",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Business Insights for Marketing",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Marketing Team",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "PLANNING_ERROR",
+        "description": "No explicit validation or verification step for analysis results is present in the trace (potential quality / correctness gap).",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Tool-related risk: pandas_analyzer is used but the trace provides no indication of scalability or limitations; this could cause execution problems on large datasets.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ],
+        "affected_id": "tool_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Introduce an explicit validation step (or a lightweight Data Verification agent) after analysis to validate insights before delivery to marketing. This reduces risk of incorrect recommendations and improves trust in outputs.",
+        "affected_ids": [
+          "agent_001",
+          "task_001",
+          "human_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Harden pandas_analyzer usage by adding data size checks, memory/performance guards, or an option to switch to a more scalable engine (e.g., Dask/Spark) for large datasets.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 250,
+  "input_trace_preview": "\n    Agent: DataAnalyzer\n    Task: Analyze customer data and generate insights\n    Tool: pandas_analyzer\n    \n    The DataAnalyzer agent processes customer data using pandas_analyzer tool\n    to generate business insights for the marketing team.\n    "
+}

extraction_analysis/cot_extraction_20250907_174131_59a97f42.json

@@ -0,0 +1,433 @@
+{
+  "timestamp": "20250907_174131",
+  "extraction_id": "59a97f42",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "STEP 1 - PROPOSITION-BASED TRACE DECOMPOSITION: Break the trace into self-contained proposition units (agents, structure, interactions, context). I extract agent existence/actions/usages, structural assertions about agents and sequence, interaction claims (calls, precedences, tool requirements), and contextual classification (domain, complexity, pattern).",
+      "output": "- AGENT PROPOSITIONS:\n  - Agent ArithmeticProgressions Expert exists (ArithmeticProgressions_Expert appears in data.agents and observations).\n  - Agent ProblemSolving Expert exists (ProblemSolving_Expert appears in data.agents and observations).\n  - Agent Verification Expert exists (Verification_Expert appears in data.agents, metadata.mistake_agent, and observations).\n  - Agent Computer Terminal exists (Computer_terminal appears in data.agents and two observations).\n  - ArithmeticProgressions Expert performs arithmetic confirmation/calculation (observed message named ArithmeticProgressions_Expert).\n  - ProblemSolving Expert provides the task plan and initial verification output (observed message named ProblemSolving_Expert).\n  - Verification Expert attempts to verify costs and reports verified values but is flagged in metadata as mistaken (observed messages named Verification_Expert and metadata.mistake_agent).\n  - Computer Terminal posts operational prompts/coordination messages (observed messages named Computer_terminal).\n\n- STRUCTURAL PROPOSITIONS:\n  - Workflow contains 4 distinct agents (agent list in content.data.agents; trace_metadata.component_stats.agent_count = 4).\n  - Interaction flow indicated in trace_metadata as \"Sequential multi-agent collaboration with 4 agents\".\n  - Input (the question) initiates the workflow (content.data.question).\n  - Final numeric output (amount saved) is produced by the collaborative process and returned before termination messages.\n\n- INTERACTION PROPOSITIONS:\n  - Agent ProblemSolving Expert posts the initial plan and an initial computed result (Observation 1).\n  - Agent Verification Expert posts a verification message and later finalizes with TERMINATE (Observations 2, 6, 7).\n  - Computer Terminal posts coordination reminders (Observations 3 and 5) indicating agents used the terminal as a coordination tool.\n  - ArithmeticProgressions Expert confirms arithmetic validity and concurs with the computed savings (Observation 4).\n  - Task precedence: verification/confirmation/calculation steps occur sequentially across agents (trace_metadata.interaction_flow and sequential observations).\n  - Tool required: Computer Terminal used to coordinate message flow (observations by Computer_terminal).\n\n- CONTEXTUAL PROPOSITIONS:\n  - Domain: This is a problem-solving/verification workflow (task: verify ticket and season pass prices and compute savings).\n  - Complexity: Workflow complexity is complex (4 distinct agents => complex per specified classification rule).\n  - Pattern: Follows a sequential execution pattern (trace_metadata indicates sequential calls and observations show stepwise messages)."
+    },
+    {
+      "explanation": "STEP 2 - MULTI-GRANULARITY HIERARCHICAL ANALYSIS: Validate JSON structure and analyze content at character, word, sentence, and document levels; fuse attention to prioritize entity/relationship extraction.",
+      "output": "- CHARACTER-LEVEL:\n  - JSON syntax appears well-formed (top-level keys: filename, title, description, trace_type, trace_source, tags, content, trace_metadata).\n  - Notable suffix patterns: \"_Expert\" and \"_terminal\" signal agent/tool roles.\n\n- WORD-LEVEL:\n  - Key tokens present: agent, agents, observations, question, verify, cost, season pass, daily ticket, savings, TERMINATE.\n  - Action verbs: verify, confirm, collect, calculate, solve.\n\n- SENTENCE-LEVEL:\n  - Clear agent declarations in observations with \"name\" and \"role\" fields.\n  - Task specification: the observations include a plan to confirm daily ticket and season pass costs and produce verified outputs.\n  - Relationship indicators: \"Given\", \"Verify\", \"Calculation of Savings\" appear in messages.\n\n- DOCUMENT-LEVEL:\n  - Workflow: sequential multi-agent collaboration to verify costs and compute savings for 4 planned visits.\n  - Domain: cost verification and arithmetic problem solving for a consumer decision (season pass vs daily tickets).\n  - Complexity: Classified as COMPLEX (4 agents > 3).\n\n- ATTENTION FUSION:\n  - Character-level weight ensures correct parsing of fields and agent name patterns.\n  - Word-level directed recognition of agent and task keywords yields entity candidates.\n  - Sentence-level extracts the PERFORMS/CONSUMED_BY/PRODUCES patterns.\n  - Document-level confirms sequential workflow structure and complexity level."
+    },
+    {
+      "explanation": "STEP 3 - SPECIALIZED JSON STRUCTURE PARSING: Systematically locate agents, tools, primary keys, and execution evidence; extract distinct agents and relationships (apply AGENT DISCOVERY PROTOCOL and RELATIONSHIP PATTERN DETECTION).",
+      "output": "- AGENT DISCOVERY:\n  - Found agents array at content.data.agents (lines ~16-22): ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert.\n  - Distinct agent count = 4 (verified via content.data.agents and trace_metadata.component_stats.agent_count).\n  - Pattern: names ending in \"_Expert\" indicate specialized roles; Computer_terminal is a tool-like participant.\n\n- STRUCTURAL ELEMENTS:\n  - Primary keys used: content.data.question (input), content.data.agents, content.observations (execution logs), content.metadata (mistake info).\n  - Observations provide evidence of agents performing tasks and the computed amounts.\n\n- RELATIONSHIP PATTERNS:\n  - Observations show sequential contributions: ProblemSolving_Expert provides plan and computed values (obs 1), Verification_Expert attempts verification (obs 2/6/7), ArithmeticProgressions_Expert validates arithmetic (obs 4), Computer_terminal coordinates (obs 3/5).\n  - No parallel/branching patterns detected; all calls are sequential.\n\n- QUALITY ASSURANCE:\n  - Agent count (4) is consistent in multiple locations (content.data.agents and trace_metadata).\n  - All agent mentions are supported by observation messages.\n  - The metadata flags a mistake (metadata.mistake_agent = Verification_Expert) which is used to identify a failure event."
+    },
+    {
+      "explanation": "STEP 4 - WORKFLOW COMPLEXITY CLASSIFICATION: Use JSON-verified agent count to classify workflow complexity.",
+      "output": "- Distinct agents = 4 → CLASSIFICATION = COMPLEX (3+ distinct agents).\n- Therefore we must produce 3 specialized tasks with 1:1 agent-task mapping and sequential NEXT relations between tasks, and include a Computer Terminal tool entity."
+    },
+    {
+      "explanation": "STEP 5 - ONTOLOGY-COMPLIANT TASK DECOMPOSITION AND UNIFIED ENTITY-RELATION EXTRACTION: Generate entities (agents, tasks, tool, input, output, human) and relations (CONSUMED_BY, PERFORMS, NEXT, PRODUCES, DELIVERS_TO, USES) consistently with ID rules and with only references to content via line numbers. raw_prompt must be set to empty string per instructions. interaction_prompt set to empty string. Ensure every relation references existing entity IDs.",
+      "output": "Entities and relations will be created below. All raw_prompt fields are empty strings and all relation interaction_prompt fields are empty strings; textual evidence is provided only via raw_prompt_ref and interaction_prompt_ref line references."
+    },
+    {
+      "explanation": "ENTITY CREATION: Create agents and tasks with canonical IDs following the rules (agent_001..agent_004, task_001..task_003, input_001, output_001, human_001). Assign importance levels: core agents and tasks HIGH, tool MEDIUM, input/output/human HIGH.",
+      "output": ""
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Season Pass Savings Verification System",
+    "system_summary": "A sequential multi-agent system that verifies ticket and season pass prices and computes the savings for planned visits. The workflow begins with the user question (input) consumed by the primary agent. Three specialist agents collaborate: Arithmetic Progressions Expert computes savings, Problem Solving Expert confirms costs and performs analysis, and Verification Expert validates price data. A Computer Terminal is used for coordination. The trace shows a mistake where the Verification Expert failed to collect price data, causing an incorrect final correctness flag.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Arithmetic Progressions Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 16,
+            "line_end": 22
+          },
+          {
+            "line_start": 86,
+            "line_end": 100
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Problem Solving Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 16,
+            "line_end": 22
+          },
+          {
+            "line_start": 31,
+            "line_end": 50
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 16,
+            "line_end": 22
+          },
+          {
+            "line_start": 51,
+            "line_end": 75
+          },
+          {
+            "line_start": 111,
+            "line_end": 124
+          }
+        ]
+      },
+      {
+        "id": "agent_004",
+        "type": "Tool",
+        "name": "Computer Terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 16,
+            "line_end": 22
+          },
+          {
+            "line_start": 76,
+            "line_end": 85
+          },
+          {
+            "line_start": 101,
+            "line_end": 110
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Savings Calculation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 31,
+            "line_end": 50
+          },
+          {
+            "line_start": 86,
+            "line_end": 100
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Cost Confirmation and Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 31,
+            "line_end": 50
+          },
+          {
+            "line_start": 51,
+            "line_end": 75
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Price Data Verification",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 51,
+            "line_end": 75
+          },
+          {
+            "line_start": 111,
+            "line_end": 124
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Visit Plan and Price Verification Question",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 16,
+            "line_end": 20
+          },
+          {
+            "line_start": 31,
+            "line_end": 40
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified Savings Amount",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 31,
+            "line_end": 50
+          },
+          {
+            "line_start": 86,
+            "line_end": 100
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Requester",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 16,
+            "line_end": 20
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 16,
+            "line_end": 22
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 86,
+            "line_end": 100
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 31,
+            "line_end": 50
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 51,
+            "line_end": 75
+          },
+          {
+            "line_start": 111,
+            "line_end": 124
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 200,
+            "line_end": 210
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 200,
+            "line_end": 210
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 31,
+            "line_end": 50
+          },
+          {
+            "line_start": 86,
+            "line_end": 100
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 121,
+            "line_end": 124
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_002",
+        "target": "agent_004",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 76,
+            "line_end": 85
+          }
+        ]
+      },
+      {
+        "id": "rel_010",
+        "source": "agent_003",
+        "target": "agent_004",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 101,
+            "line_end": 110
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Verification Expert failed to collect price data for daily tickets and season passes for California's Great America in 2024 (metadata indicates missing data collection).",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 6,
+            "line_end": 12
+          }
+        ],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "HALLUCINATION",
+        "description": "Final trace flagged as not correct (is_correct: false) indicating the system produced an incorrect final verification result.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 6,
+            "line_end": 12
+          },
+          {
+            "line_start": 31,
+            "line_end": 50
+          }
+        ],
+        "affected_id": "output_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Provide the Verification Expert with direct data retrieval tools (web access or price API integration) or a cached pricing database so price collection does not rely on manual pattern matching. Justification: metadata indicates the Verification Expert failed to collect price data.",
+        "affected_ids": [
+          "agent_003",
+          "agent_004"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 6,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Merge the arithmetic calculation and problem solving steps into a single coordinated task for straightforward price-difference problems, reducing handoffs and verification latency. Justification: the problem is arithmetic-simple (4 visits) and multiple handoffs increased risk of missing retrieval steps.",
+        "affected_ids": [
+          "agent_001",
+          "agent_002",
+          "task_001",
+          "task_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 31,
+            "line_end": 50
+          },
+          {
+            "line_start": 86,
+            "line_end": 100
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 9127,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_0.json\",\n  \"title\": \"Algorithm Sample 0: How much did I save by purchasing a season pass in...\",\n  \"description\": \"Multi-agent collaboration sample from algorithm-generated dataset. Agents: ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert. Question: How much did I save by purchasing a season pass instead of daily tickets for California's Great Amer...\",\n  \"trace_type\": \"multi_agent_collaboration\",\n  \"trace_source\": \"al..."
+}

extraction_analysis/cot_extraction_20250907_181722_6e07eb9a.json

@@ -0,0 +1,329 @@
+{
+  "timestamp": "20250907_181722",
+  "extraction_id": "6e07eb9a",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count agents, identify domain and complexity.",
+      "output": "Distinct agents found: 4 (ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert). Domain: data analysis / pricing verification (analysis of ticket/season-pass pricing). Complexity: Complex workflow (4 agents, >3)."
+    },
+    {
+      "explanation": "2) Entity extraction: classify agents, tasks, tools, inputs/outputs, human stakeholders.",
+      "output": "Agents (HIGH priority): agent_001 Verification_Expert, agent_002 ProblemSolving_Expert, agent_003 ArithmeticProgressions_Expert. Tool(s) (MEDIUM): tool_001 Computer_terminal, tool_002 Official_Website (implicit external data source). Tasks (HIGH/MEDIUM): task_001 Verify_ticket_prices, task_002 Compute_savings, task_003 Aggregate_and_conclude. Input (HIGH): input_001 Savings_question. Outputs (HIGH): output_001 Verified_costs, output_002 Savings_amount, output_003 Final_report. Human (HIGH): human_001 Requester/Manager."
+    },
+    {
+      "explanation": "3) Relation mapping: map PERFORMS, USES, PRODUCES, CONSUMED_BY, NEXT, DELIVERS_TO.",
+      "output": "Key mappings: Verification_Expert PERFORMS Verify_ticket_prices; ArithmeticProgressions_Expert PERFORMS Compute_savings; ProblemSolving_Expert PERFORMS Aggregate_and_conclude. Workflow NEXT chain: Verify_ticket_prices -> Compute_savings -> Aggregate_and_conclude. Input consumed by Verify_ticket_prices. Verify_ticket_prices PRODUCES Verified_costs; Compute_savings PRODUCES Savings_amount. Outputs DELIVERED_TO Requester/Manager. Agents USE the Computer_terminal and attempt to consult Official_Website (implicit)."
+    },
+    {
+      "explanation": "4) Quality check, failures and optimizations.",
+      "output": "Quality: All relation targets reference defined entities. Failures identified: (a) Verification_Expert failed to retrieve authoritative price data (retrieval error), (b) method relied on historical ranges rather than source data (planning/error leading to incorrect final answer $120 vs ground-truth $55). Optimizations: (1) PROMPT_REFINEMENT to require explicit source verification or unverifiable flag; (2) TOOL_ENHANCEMENT to enable a web_lookup API or merge calculation responsibilities to reduce handoffs."
+    },
+    {
+      "explanation": "5) Assemble final knowledge graph (entities, relations, failures, optimizations).",
+      "output": "See final_answer block for structured KnowledgeGraph with entities, relations, failures, and optimizations."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "SeasonPassSavings_Verification_Workflow",
+    "system_summary": "A sequential multi-agent workflow to verify ticket and season-pass prices and compute savings for planned visits. Verification_Expert validates prices (using available tools), ArithmeticProgressions_Expert computes savings, and ProblemSolving_Expert aggregates and finalizes the report; Computer_terminal and external website sources are used as tools.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "ProblemSolving_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "ArithmeticProgressions_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_002",
+        "type": "Tool",
+        "name": "Official_Website_or_Authoritative_Price_Source",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Verify_ticket_prices",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Compute_savings",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Aggregate_and_conclude",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Savings_question_(season_pass_vs_daily_tickets_for_4_visits_in_2024)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified_costs_(daily_ticket_and_season_pass_2024)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_002",
+        "type": "Output",
+        "name": "Savings_amount_calculation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_003",
+        "type": "Output",
+        "name": "Final_report_and_conclusion",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Requester/Manager",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      }
+    ],
+    "relations": [
+      {
+        "id": "relation_001",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_002",
+        "source": "agent_003",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_003",
+        "source": "agent_002",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_004",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_005",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_006",
+        "source": "task_002",
+        "target": "output_002",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_007",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_008",
+        "source": "output_002",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_009",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_010",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_011",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_012",
+        "source": "agent_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_013",
+        "source": "agent_001",
+        "target": "tool_002",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_014",
+        "source": "task_003",
+        "target": "output_003",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_015",
+        "source": "output_003",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Verification_Expert failed to collect authoritative price data for daily tickets and season passes (relied on provided/heuristic values).",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Agent relied on historical price ranges instead of retrieving authoritative sources, producing an incorrect final numeric result ($120 vs ground-truth $55).",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require verification agents to explicitly state data sources used; if web access is unavailable, flag prices as 'unverified' rather than inferring from historical ranges. This reduces hallucination and makes provenance explicit.",
+        "affected_ids": [
+          "agent_001",
+          "task_001",
+          "tool_002"
+        ],
+        "raw_text_ref": []
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Provide a dedicated web_lookup / pricing_api tool and permission for Verification_Expert to call it. Optionally consolidate ProblemSolving_Expert and ArithmeticProgressions_Expert responsibilities into a single calculation/aggregation agent to reduce handoffs and inconsistencies.",
+        "affected_ids": [
+          "tool_002",
+          "agent_002",
+          "agent_003",
+          "agent_001"
+        ],
+        "raw_text_ref": []
+      }
+    ]
+  },
+  "input_trace_length": 9127,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_0.json\",\n  \"title\": \"Algorithm Sample 0: How much did I save by purchasing a season pass in...\",\n  \"description\": \"Multi-agent collaboration sample from algorithm-generated dataset. Agents: ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert. Question: How much did I save by purchasing a season pass instead of daily tickets for California's Great Amer...\",\n  \"trace_type\": \"multi_agent_collaboration\",\n  \"trace_source\": \"al..."
+}

extraction_analysis/cot_extraction_20250907_182012_f381d4dd.json

@@ -0,0 +1,678 @@
+{
+  "timestamp": "20250907_182012",
+  "extraction_id": "f381d4dd",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: Count distinct agents and classify domain/complexity.",
+      "output": "Distinct agents found in data/observations: Computer_terminal, DataVerification_Expert, Eateries_Expert, Location-Based_Services_Expert (4 distinct). Domain: location-based services with data verification. Complexity: Complex (4 agents > 3)."
+    },
+    {
+      "explanation": "2) Entity extraction: Extract Agents, Tasks, Tools, Inputs/Outputs, Human stakeholders from the trace.",
+      "output": "Agents: Computer_terminal, DataVerification_Expert, Eateries_Expert, Location-Based_Services_Expert. Tasks inferred: Identify park location; Search for nearby eateries; Extract/compile candidate eateries; Verify operating hours; Determine closest eatery; Produce final report. Tools inferred: perform_web_search API, Python execution environment (code runner), external websites (Tripadvisor/Yelp/OpenTable). Input: user question. Outputs: candidate list, filtered list, final answer. Human: requester/manager."
+    },
+    {
+      "explanation": "3) Relation mapping: Map PERFORMS, USES, PRODUCES/CONSUMED_BY, NEXT, and DELIVERS_TO relations among entities.",
+      "output": "Created PERFORMS relations for each agent→task, USES relations connecting agents to tools used (web search function, code runner, external websites). Built NEXT chain across tasks to represent sequential workflow. Mapped PRODUCES/CONSUMED_BY relations for intermediate artifacts (candidate list → filtered list → final report)."
+    },
+    {
+      "explanation": "4) Quality check, failures and optimizations.",
+      "output": "Verified relation ids reference existing entities. Noted two failures from trace (execution failure due to None result, and incorrect Python code causing wrong final answer). Added optimizations: PROMPT_REFINEMENT (explicit error-handling and None checks) and TOOL_ENHANCEMENT (robust wrapper for perform_web_search and result validation)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Location-Based Eateries Verification (Multi-Agent)",
+    "system_summary": "A sequential multi-agent workflow that locates Harkness Memorial State Park, searches nearby eateries, verifies operating hours (filtering for those open at 11pm on Wednesdays), and returns the closest qualifying eatery. Four specialized agents collaborate using web-search and code execution tools with intermediate verification steps.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "DataVerification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Eateries_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_004",
+        "type": "Agent",
+        "name": "Location-Based_Services_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Identify location of Harkness Memorial State Park",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Search for nearby eateries using location-based services",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Extract and compile candidate eatery details (name, address, proximity)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_004",
+        "type": "Task",
+        "name": "Verify operating hours (filter for open ≥ 11:00 PM on Wednesdays)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_005",
+        "type": "Task",
+        "name": "Determine the closest eatery among filtered candidates",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_006",
+        "type": "Task",
+        "name": "Produce final report (name, address, distance, open confirmation)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "perform_web_search API (web search function)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "tool_002",
+        "type": "Tool",
+        "name": "Python execution environment / code runner",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "tool_003",
+        "type": "Tool",
+        "name": "External web sources (Tripadvisor, Yelp, OpenTable)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User question: closest eatery to Harkness Memorial State Park open at 11pm on Wednesdays",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Candidate eatery list (raw search results)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_002",
+        "type": "Output",
+        "name": "Filtered eateries (open ≥ 11pm Wed)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_003",
+        "type": "Output",
+        "name": "Final report (name, address, distance, confirmation)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Requester / Manager",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
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+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Search function returned None and caused a TypeError when iterating results (execution failed).",
+        "raw_text": "",
+        "raw_text_ref": [
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+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Incorrect Python code / validation logic in DataVerification_Expert led to an incorrect final answer (trace metadata shows mistake_agent = DataVerification_Expert).",
+        "raw_text": "",
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+      }
+    ],
+    "optimizations": [
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+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Add explicit result validation and fallback logic in DataVerification_Expert prompts and code (check for None, handle empty search results, and log partial results). This reduces EXECUTION_ERROR occurrences and clarifies error handling responsibilities.",
+        "affected_ids": [
+          "agent_002",
+          "task_004",
+          "tool_001"
+        ],
+        "raw_text_ref": [
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+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Wrap perform_web_search with a robust wrapper that returns consistent structured results (list) and includes retry/backoff and provenance metadata from external sources (Yelp/Tripadvisor/OpenTable). This reduces None results and improves traceability.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 29909,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_1.json\",\n  \"title\": \"Algorithm Sample 1: What is the closest eatery to Harkness Memorial State Park t...\",\n  \"description\": \"Complex location-based services sample with 4 specialized agents. Involves geographic queries, time-based filtering, and data verification.\",\n  \"trace_type\": \"location_based_services\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"location_services\",\n    \"data_verification\",\n    \"re..."
+}

extraction_analysis/cot_extraction_20250907_182822_ebfcaffe.json

@@ -0,0 +1,318 @@
+{
+  "timestamp": "20250907_182822",
+  "extraction_id": "ebfcaffe",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: identify distinct agents, domain, and complexity.",
+      "output": "Distinct agents found in data.agents: Computer_terminal, DataVerification_Expert, Eateries_Expert, Location-Based_Services_Expert (3 specialist experts + 1 computer tool). Domain: location-based services / data verification (customer/location search). Complexity: Complex workflow (4 participants; 3+ specialist agents)."
+    },
+    {
+      "explanation": "2) Entity extraction: extract Agents (specialists), Tools (Computer* / APIs), high-level Tasks (3), Input and Output, and Human stakeholders.",
+      "output": "Agents (3 specialists) extracted: Location-Based_Services_Expert, Eateries_Expert, DataVerification_Expert. Tools extracted: Computer_terminal (trace lists Computer_terminal) and a WebSearch/API abstraction (perform_web_search). Input: question about closest eatery open 11pm Wednesday. Output: final eatery result (name, address, distance, open confirmation). Human stakeholders: Requesting end-user and Manager (provided plan). Tasks (3 high-level): task_001 Identify park location & search for nearby eateries; task_002 Extract/compile candidate eateries and details; task_003 Verify operating hours, filter to those open at 11pm Wed, compute distances and select closest."
+    },
+    {
+      "explanation": "3) Relation mapping & quality check: map PERFORMS, NEXT, PRODUCES/CONSUMED_BY, USES and validate references. Add failures and optimization recommendations.",
+      "output": "Workflow chain built: Input -> Location-Based_Services_Expert performs task_001 -> produces candidate list -> Eateries_Expert performs task_002 (consumes candidate list) -> produces filtered list -> DataVerification_Expert performs task_003 (consumes filtered list) -> produces final answer -> delivered to end user. NEXT relations: task_001 NEXT task_002 NEXT task_003. Quality checks: all relation IDs reference existing entities; identified two failures (execution error and incorrect code/plan). Two optimizations suggested (robust error handling + task consolidation to reduce overlap)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Location-Based Eateries Multi-Agent Pipeline",
+    "system_summary": "A sequential multi-agent system (Location-Based_Services_Expert, Eateries_Expert, DataVerification_Expert) coordinated via a Computer terminal and web-search API to find the closest eatery to Harkness Memorial State Park that is open at 11 PM on Wednesdays. The workflow: locate & search -> extract candidates -> verify hours & distance -> deliver final answer to user.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Location-Based_Services_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Eateries_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "DataVerification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_002",
+        "type": "Tool",
+        "name": "WebSearch_API (perform_web_search)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Identify Park Location & Search Nearby Eateries",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Extract Candidate Eateries and Details",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Verify Operating Hours, Filter for 11pm Wednesday, Compute Distances & Select Closest",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User question: closest eatery to Harkness Memorial State Park open at 11pm on Wednesdays",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Final Answer: Eatery (name, address, distance, open confirmation)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_002",
+        "type": "Output",
+        "name": "Intermediate: Candidate Eateries List (names, addresses, links)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_003",
+        "type": "Output",
+        "name": "Intermediate: Filtered & Verified Eateries (open≥11pm Wed)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "End User (requesting party)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_002",
+        "type": "Human",
+        "name": "Manager / Task Designer (provided plan & constraints)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
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+        "raw_text": "TypeError: 'NoneType' object is not iterable",
+        "raw_text_ref": [],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Incorrect Python code / missing error handling in DataVerification_Expert's implementation led to failed verification and ultimately an incorrect final result (trace metadata marks mistake_agent DataVerification_Expert).",
+        "raw_text": "mistake_agent: DataVerification_Expert; mistake_reason: The Python code is incorrect.",
+        "raw_text_ref": [],
+        "affected_id": "agent_003"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Add explicit failure-handling and return-value checks to DataVerification_Expert prompts and code. Instruct the agent to treat None or empty web-search responses as retriable errors and fall back to alternative sources (e.g., direct site scrapes, official business pages) before concluding no matches.",
+        "affected_ids": [
+          "agent_003",
+          "tool_002"
+        ],
+        "raw_text_ref": []
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TASK_CONSOLIDATION",
+        "description": "Consolidate or clarify responsibilities between Location-Based_Services_Expert and Eateries_Expert to reduce overlapping candidate extraction steps (e.g., single agent gathers raw candidates and another strictly validates). This reduces redundant searches and speeds verification.",
+        "affected_ids": [
+          "agent_001",
+          "agent_002",
+          "task_001",
+          "task_002"
+        ],
+        "raw_text_ref": []
+      }
+    ]
+  },
+  "input_trace_length": 29909,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_1.json\",\n  \"title\": \"Algorithm Sample 1: What is the closest eatery to Harkness Memorial State Park t...\",\n  \"description\": \"Complex location-based services sample with 4 specialized agents. Involves geographic queries, time-based filtering, and data verification.\",\n  \"trace_type\": \"location_based_services\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"location_services\",\n    \"data_verification\",\n    \"re..."
+}

extraction_analysis/cot_extraction_20250907_183024_087bb608.json

@@ -0,0 +1,392 @@
+{
+  "timestamp": "20250907_183024",
+  "extraction_id": "087bb608",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "JSON structure analysis: count distinct agents in data/observations, identify domain and complexity.",
+      "output": "Distinct participants listed in data.agents: 4 total (ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert). Applying the Computer* rule (Computer_terminal treated as a Tool), there are 3 distinct human agents (ArithmeticProgressions_Expert, ProblemSolving_Expert, Verification_Expert). Domain: cost/price verification (data analysis / consumer cost analysis). Complexity: Complex (3+ agents)."
+    },
+    {
+      "explanation": "Entity extraction following priority rules: extract Agents (*_Expert), Tools (Computer_*), Tasks (3 high-level conceptual tasks), Input/Output and Human.",
+      "output": "Agents: ArithmeticProgressions_Expert, ProblemSolving_Expert, Verification_Expert. Tool: Computer_terminal. Tasks (3 high-level): Gather & Verify Price Data; Confirm & Calculate Savings; Validate Arithmetic & Finalize Conclusion. Input: the question (cost comparison / planned visits). Output: Verified costs and amount saved. Human: end user / manager receiving the summary."
+    },
+    {
+      "explanation": "Relation mapping: PERFORMS (1:1 agent->task), NEXT chaining for tasks (task1->task2->task3), essential USES of tool, and data flow relations (CONSUMED_BY, PRODUCES, DELIVERS_TO).",
+      "output": "Mapped PERFORMS for each expert to one high-level task, NEXT relations linking the three tasks, USES relation: Verification_Expert -> Computer_terminal, CONSUMED_BY: input -> first task, PRODUCES: final task -> output, DELIVERS_TO: output -> human."
+    },
+    {
+      "explanation": "Quality check: ensure all relation targets/sources exist; surface failures from metadata/observations; propose optimizations.",
+      "output": "All relation IDs reference existing entities. Two failures detected: a RETRIEVAL_ERROR (Verification_Expert couldn't collect external price data) and a PLANNING_ERROR (agents accepted provided costs without external verification). Recommendations: PROMPT_REFINEMENT for Verification_Expert to escalate when external access is unavailable; AGENT_MERGING / TASK_CONSOLIDATION to simplify trivial arithmetic handling."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "SeasonPassSavings_Verification_MultiAgent",
+    "system_summary": "A multi-agent cost-verification workflow where ProblemSolving_Expert defines plan and collects provided values, Verification_Expert attempts external validation but cannot access external sites and proceeds with given values, and ArithmeticProgressions_Expert validates arithmetic and confirms savings. Computer_terminal is the tool used/queried for retrieval. Final output is a verified-costs summary and savings delivered to the end user.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "ArithmeticProgressions_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "ProblemSolving_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 6,
+            "line_end": 7
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Gather_and_Verify_Price_Data",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Confirm_and_Calculate_Savings",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Validate_Arithmetic_and_Finalize_Conclusion",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          },
+          {
+            "line_start": 6,
+            "line_end": 7
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Cost_Comparison_Question_And_Plan",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified_Costs_and_Amount_Saved",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 4
+          },
+          {
+            "line_start": 6,
+            "line_end": 7
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "End_User/Manager",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_003",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_001",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          },
+          {
+            "line_start": 6,
+            "line_end": 7
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 7
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 3
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Verification_Expert fails to collect external price data (cannot access external websites), preventing authoritative verification.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Agents accept provided price values and proceed without external confirmation, producing an incorrect 'is_correct' outcome in metadata.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 1,
+            "line_end": 2
+          },
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require the Verification_Expert prompt to (a) explicitly attempt retrieval, (b) if retrieval is unavailable, escalate or request verified inputs from a trusted human, and (c) annotate when provided values are used as assumptions. This prevents silent reliance on unverified inputs.",
+        "affected_ids": [
+          "agent_003",
+          "tool_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "AGENT_MERGING",
+        "description": "Merge ArithmeticProgressions_Expert into ProblemSolving_Expert (or consolidate their responsibilities) because the arithmetic step is trivial and merging reduces coordination overhead and simplifies the workflow.",
+        "affected_ids": [
+          "agent_001",
+          "agent_002",
+          "task_002",
+          "task_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          },
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 9127,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_0.json\",\n  \"title\": \"Algorithm Sample 0: How much did I save by purchasing a season pass in...\",\n  \"description\": \"Multi-agent collaboration sample from algorithm-generated dataset. Agents: ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert. Question: How much did I save by purchasing a season pass instead of daily tickets for California's Great Amer...\",\n  \"trace_type\": \"multi_agent_collaboration\",\n  \"trace_source\": \"al..."
+}

extraction_analysis/cot_extraction_20250907_183121_e8294c1e.json

@@ -0,0 +1,323 @@
+{
+  "timestamp": "20250907_183121",
+  "extraction_id": "e8294c1e",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count distinct agents, identify domain and complexity.",
+      "output": "Distinct agents found in data.observations/agents: 3 specialist agents (DataVerification_Expert, Eateries_Expert, Location-Based_Services_Expert) plus a Computer terminal listed as Computer_terminal (treated as a Tool). Domain: location-based services / data verification (customer query for eateries). Complexity: Complex multi-agent workflow (4 components listed; 3 specialist agents → complex)."
+    },
+    {
+      "explanation": "2) Entity extraction: select high-priority entities (Agents, Tasks, Tools, Input/Output, Human). Create 3 high-level tasks matching specialist roles.",
+      "output": "Agents: DataVerification_Expert, Eateries_Expert, Location-Based_Services_Expert. Tool: Computer_terminal (search/code execution tool). Input: User question about closest eatery open at 11pm on Wednesdays. Output: Structured eatery answer (name, address, distance, confirmation). Human: Requester/Manager (task requester). Tasks (high-level): task_001 IdentifyParkAndSearchEateries, task_002 ExtractAndAggregateEateryDetails, task_003 VerifyHoursAndSelectClosest."
+    },
+    {
+      "explanation": "3) Relation mapping: assign each agent to one task (PERFORMS); add task sequencing (NEXT) and data flow relations (CONSUMED_BY, PRODUCES, DELIVERS_TO, USES).",
+      "output": "PERFORMS: Location-Based_Services_Expert→task_001; Eateries_Expert→task_002; DataVerification_Expert→task_003. NEXT: task_001→task_002, task_002→task_003. USES: each task/agent uses Computer_terminal for web/search/code execution. CONSUMED_BY: Input→task_001. PRODUCES: task_003→Output. DELIVERS_TO: Output→Human."
+    },
+    {
+      "explanation": "4) Quality check: verify IDs, ensure complete linear workflow Input→Agent→Task→Output→Human, and extract failures & optimizations from trace metadata and runtime logs.",
+      "output": "All relation source/targets reference defined entity IDs. Workflow complete. Two detected failures and two optimization recommendations included."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Location-Based Eatery Discovery (Multi-Agent)",
+    "system_summary": "A sequential multi-agent location-based services workflow to find the closest eatery to Harkness Memorial State Park that is open at 11 PM on Wednesdays. Three specialist agents collaborate: a Location-Based Services expert to locate and search, an Eateries expert to extract candidate details, and a Data Verification expert to verify hours and select the closest result. A Computer terminal tool is used for web searches and code execution.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Location-Based_Services_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Eateries_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "DataVerification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "UserQuestion_ClosestEatery_Open11pm_Wednesday",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "EateryDetailResult (name, address, distance, open_confirmation)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Requester/Manager",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "IdentifyParkAndSearchEateries",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "ExtractAndAggregateEateryDetails",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "VerifyHoursAndSelectClosest",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_005",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_006",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_010",
+        "source": "agent_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_011",
+        "source": "agent_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "AGENT_ERROR",
+        "description": "DataVerification_Expert executed Python that produced a TypeError due to perform_web_search returning None.",
+        "raw_text": "TypeError: 'NoneType' object is not iterable (perform_web_search returned None)",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Initial search radius/filters were too narrow; initial results did not include eateries open until 11 PM on Wednesdays.",
+        "raw_text": "None of the eateries identified near Harkness Memorial State Park meet the requirement of being open until 11 PM on Wednesdays.",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Strengthen DataVerification_Expert prompts and include defensive coding patterns: check perform_web_search return values, handle None/empty results gracefully, and add retry/backoff logic. This reduces AGENT_ERROR risk and increases robustness.",
+        "affected_ids": [
+          "agent_003",
+          "failure_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enhance the Computer_terminal/web-search tool to always return an iterable (e.g., empty list) instead of None on failures and expose status codes. Also add a broad-radius fallback search mode to reduce planning failures when immediate local results do not meet constraints.",
+        "affected_ids": [
+          "tool_001",
+          "task_001",
+          "failure_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 29909,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_1.json\",\n  \"title\": \"Algorithm Sample 1: What is the closest eatery to Harkness Memorial State Park t...\",\n  \"description\": \"Complex location-based services sample with 4 specialized agents. Involves geographic queries, time-based filtering, and data verification.\",\n  \"trace_type\": \"location_based_services\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"location_services\",\n    \"data_verification\",\n    \"re..."
+}

extraction_analysis/cot_extraction_20250907_183235_50e78e5a.json

@@ -0,0 +1,350 @@
+{
+  "timestamp": "20250907_183235",
+  "extraction_id": "50e78e5a",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count distinct agents and classify domain/complexity.",
+      "output": "Detected 4 distinct agents (Probability_Expert, TheoreticalChemistry_Expert, Verification_Expert, Computer_terminal). Domain: research/simulation (probability & theoretical chemistry). Complexity: Complex (4 agents > 3)."
+    },
+    {
+      "explanation": "2) Entity extraction: identify Agents (exclude Computer_terminal), Tools, Inputs/Outputs, Humans, and define 3 high-level tasks appropriate for a complex multi-expert simulation workflow.",
+      "output": "Entities defined: 3 Agent entities, 1 Tool (Computer_terminal), 3 Tasks (Simulation Implementation & Analysis; Model Specification & Mechanics Validation; Result Verification & Consensus), 1 Input (Riddle/Game specification), 1 Output (Selected ball = 2), 1 Human (Contestant)."
+    },
+    {
+      "explanation": "3) Relation mapping: assign PERFORMS relations (1:1 agent->task), NEXT relations between tasks (task flow), associate tool usage, map data flow Input->Task->Output->Human, and record interaction evidence fields as empty per instructions.",
+      "output": "Mapped relations: Probability_Expert PERFORMS simulate & analyze (uses Computer_terminal); TheoreticalChemistry_Expert PERFORMS model specification; Verification_Expert PERFORMS verification; task sequence: Simulation -> Model Validation -> Verification; Input consumed by Simulation task; Simulation produces selected ball output; Output delivered to Contestant."
+    },
+    {
+      "explanation": "4) Quality check & risk identification: verify all relation IDs reference existing entities and add failures & optimizations based on trace metadata ('mistake_agent' and 'is_correct': false).",
+      "output": "Added two failures (AGENT_ERROR for Probability_Expert implementation mistake; EXECUTION_ERROR risk tied to simulation execution). Two optimization recommendations: add TOOL_ENHANCEMENT (test harness / deterministic tests) and PROMPT_REFINEMENT (explicit validation steps & cross-check)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "PickThatPingPong_Simulation_Workflow",
+    "system_summary": "Cross-disciplinary simulation workflow where domain experts design, implement, and verify a stochastic game simulation to identify the highest-ejection-probability ping-pong ball. The pipeline uses a Computer_terminal tool for execution and produces a single actionable output (selected ball number).",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Probability_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "TheoreticalChemistry_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Simulation Implementation & Statistical Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Model Specification & Mechanics Validation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Result Verification & Consensus",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Riddle: Game specification & initial ramp state (100 balls, platform of 3)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Selected ball number (simulation result) — 2",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Contestant (end user receiving selection advice)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "task_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "AGENT_ERROR",
+        "description": "Probability_Expert made an implementation mistake in the simulation, producing an incorrect outcome.",
+        "raw_text": "metadata.mistake_agent = \"Probability_Expert\"; mistake_reason: \"The agent made an error in the simulation implementation, resulting in an incorrect outcome.\"",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Simulation execution produced a result flagged as incorrect (is_correct = false, ground_truth = 3) indicating execution or validation failure.",
+        "raw_text": "metadata.is_correct = false; metadata.ground_truth = \"3\"",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add a test harness and deterministic unit tests for the simulation (fixed seeds, small-case enumerations) and CI checks on the Computer_terminal execution to catch implementation errors before full-scale runs.",
+        "affected_ids": [
+          "tool_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine the task and validation prompts to require explicit cross-checks against analytical reasoning (e.g., small-N exhaustive runs, sanity checks) and mandate independent verification by Verification_Expert prior to final conclusion.",
+        "affected_ids": [
+          "task_001",
+          "task_003",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 16685,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_3.json\",\n  \"title\": \"Algorithm Sample 3: Here's a fun riddle that I think you'll enjoy.\\n\\nYou have bee...\",\n  \"description\": \"Cross-disciplinary collaboration between probability and theoretical chemistry experts solving complex riddle scenarios\",\n  \"trace_type\": \"probability_game_theory\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"probability\",\n    \"theoretical_chemistry\",\n    \"game_theory\",\n    \"sim..."
+}

extraction_analysis/cot_extraction_20250907_183347_d1f74ddf.json

@@ -0,0 +1,252 @@
+{
+  "timestamp": "20250907_183347",
+  "extraction_id": "d1f74ddf",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "JSON structure analysis: count distinct agents in data/observations, identify domain and classify complexity.",
+      "output": "Found 4 distinct agents listed in data.agents (Computer_terminal, Literary_Analysis_Expert, NorseMythology_Expert, Verification_Expert). Domain: academic literature analysis / scholarly research. Complexity: Complex multi-agent workflow (4 agents > 3)."
+    },
+    {
+      "explanation": "Entity extraction: identify high-priority Agents, Tools, Tasks, Input/Output, and Human recipient per extraction rules.",
+      "output": "Agents (3 experts) extracted: Literary_Analysis_Expert, NorseMythology_Expert, Verification_Expert. Tools extracted: Computer_terminal (Computer* -> Tool) and arxiv_search API (invoked function). Tasks (3 high-level): (1) Confirm journal context (Norse myth mapping), (2) Locate article (search & retrieval), (3) Verify quoted word (analysis & validation). Input: research question. Output: verified quoted word ('clichéd'). Human: Manager / Requesting researcher (receives the verified result)."
+    },
+    {
+      "explanation": "Relation mapping: create PERFORMS relations (1:1 Agent->Task), NEXT relations between tasks, single workflow flow (Input->...->Output->Human), and essential USES relations for tools.",
+      "output": "Mapped PERFORMS for each agent to a single task, NEXT links: Confirm journal -> Locate article -> Verify quoted word. Input consumed by first task, final task produces Output which is delivered to Human. Locate article uses arxiv_search and Computer_terminal (evidence shows arxiv_search was invoked)."
+    },
+    {
+      "explanation": "Quality check: validate relation references, ensure end-to-end workflow, and extract failures + optimizations from trace (metadata shows a planning mistake).",
+      "output": "All relation IDs reference existing entity IDs. Workflow covers Input -> Agent tasks -> Output -> Human. Two failures identified (inappropriate tool selection; retrieval mismatch). Two optimization recommendations provided (prompt/tool changes)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Academic Literature Multi-Agent Verification System",
+    "system_summary": "Multi-agent scholarly research workflow combining literary-analysis and Norse-mythology expertise to locate and verify a quoted word in a specified journal article. The workflow uses a computer terminal and a targeted search API (arxiv_search) but shows a planning error where an academic humanities journal was searched via an arXiv-focused tool.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Literary_Analysis_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "NorseMythology_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_002",
+        "type": "Tool",
+        "name": "arxiv_search_API",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Confirm_Journal_Context",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Locate_Article",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Verify_Quoted_Word",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Research_Query",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified_Quoted_Word_clichéd",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Requesting_Manager",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "agent_001",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_004",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_005",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_006",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_009",
+        "source": "task_002",
+        "target": "tool_002",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_010",
+        "source": "task_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Inappropriate tool selection: Literary_Analysis_Expert chose arxiv_search for a humanities journal (Fafnir), which is outside arXiv's typical coverage.",
+        "raw_text": "mistake_reason: The code calls the arxiv_search tool; however, using other tools, such as perform_web_search, would be more appropriate because arXiv is primarily academically oriented.",
+        "raw_text_ref": [],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Search results from arXiv returned unrelated entries and did not locate the target article in Fafnir.",
+        "raw_text": "Execution output: arXiv entries returned (FAFNIR accelerator/neutron facility papers and other 2014 items) but no Emily Midkiff Fafnir article.",
+        "raw_text_ref": [],
+        "affected_id": "tool_002"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require agents to check the coverage/scope of a chosen search tool before invoking it (e.g., verify that a humanities journal is indexed by arXiv). Add a pre-check step into the Literary_Analysis_Expert prompt to select appropriate humanities databases when the target journal is literary.",
+        "affected_ids": [
+          "agent_001",
+          "task_002",
+          "tool_002"
+        ],
+        "raw_text_ref": []
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Integrate or provide access to humanities-focused databases (e.g., JSTOR, Project MUSE, the journal's official archive) as tools available to the workflow, and prefer them for literature/journal queries.",
+        "affected_ids": [
+          "tool_002",
+          "task_002"
+        ],
+        "raw_text_ref": []
+      }
+    ]
+  },
+  "input_trace_length": 18452,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_14.json\",\n  \"title\": \"Algorithm Sample 14: In Emily Midkiff's June 2014 article in a journal named for ...\",\n  \"description\": \"Scholarly research system combining literary analysis and Norse mythology expertise for academic paper investigation\",\n  \"trace_type\": \"academic_literature_analysis\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"academic_research\",\n    \"literature_analysis\",\n    \"norse_mythology\"..."
+}

extraction_analysis/cot_extraction_20250907_183500_679287a2.json

@@ -0,0 +1,336 @@
+{
+  "timestamp": "20250907_183500",
+  "extraction_id": "679287a2",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) Analyze JSON structure: count distinct agents, identify domain and complexity.",
+      "output": "Distinct agents found: 4 (Computer_terminal, DataAnalysis_Expert, DataVerification_Expert, StatisticalAnalysis_Expert). Domain: wildlife research / ecological data analysis. Complexity: Complex workflow (>=3 agents)."
+    },
+    {
+      "explanation": "2) Extract entities following priorities: Agents (specialists), Tools (Computer* and external DB), Inputs/Outputs, Human, and 3 high-level tasks for a multi-agent workflow.",
+      "output": "Agents (3 Experts), Tools (Computer_terminal, USGS NAS database), Input (analysis question & source instruction), Output (species count result), Human (Manager/Stakeholder), Tasks (Locate & Acquire Dataset; Extract & Analyze Records; Verify & Report Findings)."
+    },
+    {
+      "explanation": "3) Map relations: each agent performs one primary task; tasks are sequenced with NEXT relations; tools used by acquisition/analysis tasks; workflow flow from Input -> tasks -> Output -> Human.",
+      "output": "Mapped PERFORMS, NEXT, CONSUMED_BY, USES, PRODUCES, DELIVERS_TO relations with importance levels. Interaction evidence exists (CSV parsing error, HTML placeholder download) and is recorded as failures."
+    },
+    {
+      "explanation": "4) Quality check: verify IDs cross-reference, ensure Input→Agent→Task→Output→Human flow, add 1-2 failures and optimization recommendations.",
+      "output": "All relation source/target IDs reference entities. Two failures (parser error, wrong placeholder download) and two optimization recommendations included."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Wildlife Ecological Data Multi-Agent Analysis",
+    "system_summary": "A sequential multi-agent workflow for locating, downloading, analyzing, and verifying USGS nonindigenous aquatic species data to answer a species-count question (crocodiles in Florida, 2000–2020). Three domain experts coordinate acquisition, analysis, and verification while using a computer terminal and the USGS NAS database.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataAnalysis_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "DataVerification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "StatisticalAnalysis_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "tool_002",
+        "type": "Tool",
+        "name": "USGS_NAS_Database_API",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Question: Count of nonindigenous crocodiles in Florida (2000-2020) with USGS NAS source instruction",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Reported count of nonindigenous crocodiles found in Florida (2000-2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Manager / Wildlife Research Stakeholder",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Locate & Acquire Dataset (confirm exact dataset name and URL; download CSV)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Extract & Analyze Species Records (filter by species=nonindigenous crocodile, location=Florida, years=2000-2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Verify & Report Findings (validate counts, interpret results, deliver report)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_005",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_006",
+        "source": "agent_002",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_007",
+        "source": "task_001",
+        "target": "tool_002",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_008",
+        "source": "task_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_009",
+        "source": "task_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_010",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_011",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Downloaded file was an HTML page (placeholder), indicating the dataset URL was incorrect or unresolved.",
+        "raw_text": "head output shows HTML (<!doctype html> ...), indicating the file is not a CSV.",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "tool_002"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "CSV parsing failed when attempting to read the downloaded file (pandas ParserError: Expected 1 fields in line 8, saw 2).",
+        "raw_text": "pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 8, saw 2",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "tool_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require the acquisition step to include an explicit, validated dataset URL or API endpoint (and a checksum or content-type check) in the initial plan to avoid placeholder downloads. Justification: the workflow failed due to an unresolved/placeholder URL and an HTML response.",
+        "affected_ids": [
+          "task_001",
+          "agent_001",
+          "tool_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add an automated pre-download verification step (HEAD request, content-type check, simple CSV header validation) in the Computer_terminal toolchain to detect non-CSV responses before attempting parsing. Justification: prevents wasted parsing attempts and clarifies retrieval errors earlier.",
+        "affected_ids": [
+          "tool_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 13048,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_16.json\",\n  \"title\": \"Algorithm Sample 16: How many nonindigenous crocodiles were found in Florida from...\",\n  \"description\": \"Statistical analysis system for ecological data processing, focusing on invasive species tracking and government dataset analysis\",\n  \"trace_type\": \"wildlife_data_analysis\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"data_analysis\",\n    \"wildlife_research\",\n    \"statistical_ana..."
+}

extraction_analysis/cot_extraction_20250907_183552_f4c10ebc.json

@@ -0,0 +1,190 @@
+{
+  "timestamp": "20250907_183552",
+  "extraction_id": "f4c10ebc",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count distinct agents, identify domain, classify complexity.",
+      "output": "Distinct agents found: 1 (trace metadata \"agent_count\": 1). Domain: programming documentation / educational assistance (RAG-powered documentation assistant). Complexity: Simple workflow (1 agent)."
+    },
+    {
+      "explanation": "2) Entity extraction: extract Agents, single high-level Task (since workflow is simple), Tools (Computer* => tools), Input/Output, Human stakeholder.",
+      "output": "Entities selected: agent_001 (Python_Programming_Assistant), task_001 (RAG_Powered_Documentation_Assistance), tool_001 (Document_Retrieval_System), tool_002 (LLM_gpt-4o-2024-11-20), input_001 (User_Query_list_comprehensions), output_001 (Explanation_and_Code_Examples), human_001 (Beginner_User)."
+    },
+    {
+      "explanation": "3) Relation mapping: create 1:1 PERFORMS mapping (agent→task), map input consumption, tool usage, task production and delivery to human. Keep relations minimal and high-priority.",
+      "output": "Relations created: agent_001 PERFORMS task_001; input_001 CONSUMED_BY task_001; task_001 USES tool_001 and tool_002; task_001 PRODUCES output_001; output_001 DELIVERS_TO human_001."
+    },
+    {
+      "explanation": "4) Risk/failure identification: find potential failure modes visible in the trace (explicit failures none, infer retrieval/LLM risks).",
+      "output": "Two risk events identified: a potential retrieval limitation (RETRIEVAL_ERROR) and an LLM hallucination/performance-claim risk (HALLUCINATION)."
+    },
+    {
+      "explanation": "5) Optimizations: propose targeted recommendations (prompt refinement and tool enhancement) to reduce risks and improve trace robustness.",
+      "output": "Recommendations: require citation/source linking in LLM outputs (PROMPT_REFINEMENT) and add stricter document-ranking / provenance in the retrieval tool (TOOL_ENHANCEMENT)."
+    },
+    {
+      "explanation": "6) Quality check: ensure all relation source/target IDs exist and full workflow Input→Agent→Task→Output→Human is present.",
+      "output": "All relations reference existing entities. Workflow completeness satisfied: input_001 → (agent_001 → task_001) → output_001 → human_001."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Python Documentation Assistant (RAG)",
+    "system_summary": "A single-agent RAG-powered Python documentation assistant that retrieves relevant docs, generates concise explanations and code examples, and returns results to a beginner user. The system uses a document retrieval component and an LLM to synthesize and produce outputs.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Python_Programming_Assistant",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "RAG_Powered_Documentation_Assistance",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Document_Retrieval_System",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_002",
+        "type": "Tool",
+        "name": "LLM_gpt-4o-2024-11-20",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User_Query_list_comprehensions",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Explanation_and_Code_Examples",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Beginner_User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_002",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_004",
+        "source": "task_001",
+        "target": "tool_002",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_006",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Document retrieval might miss niche or updated references; depends on relevance ranking and coverage of the knowledge base.",
+        "raw_text": "Found relevant documentation: [...] (metadata.relevance_score: 0.94) - retrieval quality depends on documents indexed and ranking",
+        "raw_text_ref": [],
+        "affected_id": "tool_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "HALLUCINATION",
+        "description": "LLM outputs may include unsupported performance claims or synthesized examples without explicit provenance (e.g., 'typically 20-30% faster').",
+        "raw_text": "List comprehensions are not only more concise but also typically 20-30% faster than equivalent for loops!",
+        "raw_text_ref": [],
+        "affected_id": "tool_002"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require the assistant to include explicit source citations and a short provenance line for any factual or performance claim. This reduces hallucination risk and improves user trust.",
+        "affected_ids": [
+          "agent_001",
+          "tool_002"
+        ],
+        "raw_text_ref": []
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add a document-ranking cutoff and include document metadata (title, URL, excerpt) with retrieval results. Surface relevance thresholds and fallback behavior when few high-relevance docs are found.",
+        "affected_ids": [
+          "tool_001"
+        ],
+        "raw_text_ref": []
+      }
+    ]
+  },
+  "input_trace_length": 10504,
+  "input_trace_preview": "{\n  \"filename\": \"python_documentation_inquiry.json\",\n  \"title\": \"Python Documentation Assistant Demo\",\n  \"description\": \"Comprehensive example showing RAG-powered AI assistant handling multi-turn programming inquiry with knowledge search, detailed explanations, code examples, performance analysis, and interactive learning\",\n  \"trace_type\": \"documentation_search\",\n  \"trace_source\": \"sample_data\",\n  \"tags\": [\n    \"programming\",\n    \"rag_assistant\",\n    \"documentation\",\n    \"failure_detection\",\n   ..."
+}

extraction_analysis/cot_extraction_20250907_183803_42f3349d.json

@@ -0,0 +1,272 @@
+{
+  "timestamp": "20250907_183803",
+  "extraction_id": "42f3349d",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count distinct agents, identify domain and complexity.",
+      "output": "Distinct agents found (from data.agents and observation 'name' fields): 4 total — ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert. Domain: pricing verification / data analysis (verification + arithmetic). Complexity: Complex workflow (4 agents > 3)."
+    },
+    {
+      "explanation": "2) Entity extraction: classify Agents (*_Expert), Tools (Computer_*), Input/Output, Human, and derive 2-3 specialized tasks because workflow is multi-step and sequential (verification -> calculation -> consolidation).",
+      "output": "Agents extracted: ArithmeticProgressions_Expert, ProblemSolving_Expert, Verification_Expert (Computer_terminal treated as Tool). Tasks (3): Data Verification (verify ticket/season pass prices), Savings Calculation (compute multi-visit cost vs pass), Plan & Consolidation (orchestrate plan, validate & finalize). Input: question prompt. Output: verified costs + computed amount saved. Human stakeholder: Manager/Requester (mentioned in observations)."
+    },
+    {
+      "explanation": "3) Relation mapping: map PERFORMS (Agent->Task), NEXT (Task->Task for sequential steps), USES (Agent->Tool), CONSUMED_BY/PRODUCES/DELIVERS_TO for data flow. Ensure maximum of 2 NEXT relations for 3 tasks.",
+      "output": "Mapped relations: Verification_Expert PERFORMS Data Verification; ArithmeticProgressions_Expert PERFORMS Savings Calculation; ProblemSolving_Expert PERFORMS Plan & Consolidation. Task sequence: Data Verification -> Savings Calculation -> Plan & Consolidation (two NEXT relations). Input consumed by Data Verification; Savings Calculation produces final numeric output; final Output delivered to Manager. All agents use Computer_terminal as a communication/execution tool."
+    },
+    {
+      "explanation": "4) Quality check: ensure all relation IDs reference defined entities, check completeness Input→Agent→Task→Output→Human, and locate failures and optimization opportunities from trace metadata.",
+      "output": "All relation endpoints reference existing entity IDs. Complete workflow chain established: input_001 -> (verification by agent_003) -> task_001 -> task_002 -> task_003 -> output_001 -> human_001. Failures found in metadata: Verification_Expert failed to collect authoritative price data; results contradict ground truth. Optimizations recommended: require explicit source citations in verification task; consider merging or clarifying handoffs between ProblemSolving and Arithmetic agents to reduce redundant confirmations."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "SeasonPassSavings_Verification_MultiAgent",
+    "system_summary": "A sequential multi-agent workflow to verify ticket and season-pass prices, compute savings for a planned four-visit summer, and consolidate results. Three expert agents perform verification, arithmetic calculation, and orchestration while a computer terminal tool supports communication/execution.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "ArithmeticProgressions_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "ProblemSolving_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Data Verification (daily ticket & season pass prices - CA Great America 2024)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Savings Calculation (compute total cost for 4 visits vs season pass)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Plan & Consolidation (orchestrate steps, validate results, finalize output)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Question: savings by purchasing season pass vs daily tickets (visits in Jun-Jul-Aug-Sep 2024)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified prices and computed amount saved (final reported result)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Manager / Requester",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_004",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_007",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_008",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_009",
+        "source": "task_002",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_010",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_011",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_012",
+        "source": "agent_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_013",
+        "source": "agent_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Verification_Expert failed to collect authoritative price data for daily tickets and season passes (did not retrieve or cite external sources).",
+        "raw_text": "The agent fails to collect price data for the daily tickets and season passes for California's Great America in 2024.",
+        "raw_text_ref": [],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "HALLUCINATION",
+        "description": "Reported verified prices ($60 and $120) conflict with ground truth ($55) leading to incorrect computed savings.",
+        "raw_text": "ground_truth: $55; is_correct: false",
+        "raw_text_ref": [],
+        "affected_id": "output_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require the Verification task prompt to mandate authoritative source retrieval and explicit citations (URLs or official pricing snapshots) before marking prices as 'verified'. This reduces retrieval errors and hallucinations.",
+        "affected_ids": [
+          "task_001",
+          "agent_003"
+        ],
+        "raw_text_ref": []
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Consolidate final arithmetic validation and result consolidation to a single agent (merge ProblemSolving_Expert and ArithmeticProgressions_Expert responsibilities or define an explicit handoff protocol) to reduce redundant confirmations and speed up finalization.",
+        "affected_ids": [
+          "agent_001",
+          "agent_002",
+          "task_002",
+          "task_003"
+        ],
+        "raw_text_ref": []
+      }
+    ]
+  },
+  "input_trace_length": 9127,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_0.json\",\n  \"title\": \"Algorithm Sample 0: How much did I save by purchasing a season pass in...\",\n  \"description\": \"Multi-agent collaboration sample from algorithm-generated dataset. Agents: ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert. Question: How much did I save by purchasing a season pass instead of daily tickets for California's Great Amer...\",\n  \"trace_type\": \"multi_agent_collaboration\",\n  \"trace_source\": \"al..."
+}

extraction_analysis/cot_extraction_20250907_183937_f253cf74.json

@@ -0,0 +1,303 @@
+{
+  "timestamp": "20250907_183937",
+  "extraction_id": "f253cf74",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Count distinct agents listed in observations/data.",
+      "output": "Distinct agents (4): ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert."
+    },
+    {
+      "explanation": "Identify domain and main goal of the trace.",
+      "output": "Domain: Pricing verification and simple savings arithmetic. Main goal: Verify ticket and season-pass costs for California's Great America (summer 2024) and compute savings for 4 visits."
+    },
+    {
+      "explanation": "Decide task structure (unified goal vs sequential).",
+      "output": "Unified verification/analysis goal: single consolidated task 'Verify Season Pass Savings' with multiple collaborating expert agents and one Computer tool (no multi-step sequential decomposition required)."
+    },
+    {
+      "explanation": "Extract entities (agents, tool, input/output, human, task) and assign IDs.",
+      "output": "Entities extracted: 3 Experts (agents), 1 Computer_terminal (Tool), 1 Input (question), 1 Task (verification + compute savings), 1 Output (verified costs & savings), 1 Human (group manager/stakeholder)."
+    },
+    {
+      "explanation": "Map relations according to core extraction principles (PERFORMS, USES, CONSUMED_BY, PRODUCES, DELIVERS_TO).",
+      "output": "PERFORMS: each expert -> Task. USES: Task -> Computer_terminal (tool). CONSUMED_BY: Input -> Task. PRODUCES: Task -> Output. DELIVERS_TO: Output -> Human."
+    },
+    {
+      "explanation": "Identify failures and optimizations from trace metadata and observations.",
+      "output": "Failures: Verification_Expert failed to collect price data and accepted provided costs leading to incorrect final savings. Recommendations: require explicit external-evidence verification (prompt refinement) and consolidate verification/problem-solving responsibilities to reduce handoff errors."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "SeasonPassSavings_Verification_System",
+    "system_summary": "A small multi-expert system to verify ticket and season-pass pricing for California's Great America (summer 2024) and compute savings for a planned set of visits. Three expert agents collaborate on a single verification + arithmetic task while a computer terminal tool is available for data retrieval; the system failed due to lack of actual price retrieval by the Verification_Expert.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "ProblemSolving_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "ArithmeticProgressions_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Verify Season Pass Savings (verify costs & compute savings)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User question: savings from season pass vs daily tickets (June–Sep 2024, 4 visits)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified costs (daily ticket, season pass) and computed savings",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "GroupChat_Manager / Requesting User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "task_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Verification_Expert failed to collect external price data and instead accepted provided prices without retrieval.",
+        "raw_text": "mistake_reason: The agent fails to collect price data for the daily tickets and season passes for California's Great America in 2024.",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "HALLUCINATION",
+        "description": "Final verified costs and computed savings are incorrect (accepted unverified inputs), producing an incorrect savings value ($120) compared to ground truth ($55).",
+        "raw_text": "metadata.ground_truth: $55; trace conclusion: Amount saved = $120",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "output_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require explicit evidence for price verification in the Verification_Expert prompt (e.g., mandate citing official source URLs or cached authoritative records). This reduces acceptance of unverifiable provided values.",
+        "affected_ids": [
+          "agent_003",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "AGENT_MERGING",
+        "description": "Consolidate ProblemSolving_Expert and Verification_Expert responsibilities or introduce a single verification-and-retrieval specialist to eliminate handoff errors where verification assumes retrieval was performed.",
+        "affected_ids": [
+          "agent_001",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 9127,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_0.json\",\n  \"title\": \"Algorithm Sample 0: How much did I save by purchasing a season pass in...\",\n  \"description\": \"Multi-agent collaboration sample from algorithm-generated dataset. Agents: ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert. Question: How much did I save by purchasing a season pass instead of daily tickets for California's Great Amer...\",\n  \"trace_type\": \"multi_agent_collaboration\",\n  \"trace_source\": \"al..."
+}

extraction_analysis/cot_extraction_20250907_184113_4eb991dd.json

@@ -0,0 +1,286 @@
+{
+  "timestamp": "20250907_184113",
+  "extraction_id": "4eb991dd",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count distinct agents, identify domain and main goal, decide task structure.",
+      "output": "Distinct agents found: 4 (ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert). Domain: cost verification / simple arithmetic savings calculation. Main goal: Verify provided ticket prices for California's Great America (summer 2024) and compute savings for 4 visits — unified verification/analysis task. Decision: Use a single consolidated Task (verification + calculation) with multiple agents collaborating."
+    },
+    {
+      "explanation": "2) Entity extraction following priority rules: map agents, tools, input/output, and task.",
+      "output": "Entities selected: 4 Agents (HIGH priority), 1 Tool (Computer_terminal), 1 consolidated Task (Verify Ticket Pricing and Calculate Season-Pass Savings), 1 Input (question), 1 Output (verified prices and computed savings), 1 Human (end user)."
+    },
+    {
+      "explanation": "3) Relation mapping: PERFORMS assignments, tool usage, data flow from input→task→output→human. Include PRODUCES/DELIVERS_TO and indicate the tool dependency. Keep interaction prompt/raw_prompt fields empty and use reference placeholders where appropriate.",
+      "output": "PERFORMS: each expert agent → task. USES: task → Computer_terminal (tool). CONSUMED_BY: input → task. PRODUCES: task → output. DELIVERS_TO: output → human. All relation IDs reference existing entities."
+    },
+    {
+      "explanation": "4) Quality check and risk identification: verify references and include failures + optimizations. Use metadata evidence to identify the failure.",
+      "output": "Detected failure: Verification_Expert failed to collect price data (retrieval error) which led to an incorrect final answer (ground_truth indicates $55, system result incorrect). Recommendations: refine prompts to require explicit sourcing and enable tool/web retrieval capability or explicit fallbacks."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Season-Pass Savings Verification AgentGraph",
+    "system_summary": "A small multi-agent verification workflow that checks provided ticket and season-pass prices for California's Great America (summer 2024) and computes savings for planned visits. Four expert agents collaborate on a single verification-and-calculation task; a Computer_terminal tool is used for data access duties (noting a retrieval failure in the trace).",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "ArithmeticProgressions_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "ProblemSolving_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_004",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Verify Ticket Pricing and Calculate Season-Pass Savings (Summer 2024)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Question: season pass vs daily tickets (4 visits in June–Sept 2024)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified costs (daily ticket, season pass) and computed savings",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "End User / Question Asker",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_007",
+        "source": "task_001",
+        "target": "agent_004",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Verification_Expert failed to collect authoritative price data for daily tickets and season passes (no external price retrieval), causing an incorrect verification result.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Task plan relied on using external sources but lacked explicit fallback instructions when external retrieval was unavailable, leading agents to accept plausible historical ranges instead of concrete citations.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require explicit evidence-sourcing steps in the task prompt: mandate fetching primary sources (official park pricing or archived pages) and include an explicit fallback policy when web retrieval is unavailable (e.g., mark verification as 'plausible but unverified'). This reduces acceptance of unverifiable historical-range claims.",
+        "affected_ids": [
+          "task_001",
+          "agent_003",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enhance Computer_terminal capabilities (or integrate a web-retrieval tool) and explicitly route data-collection responsibilities to it, with clear error signals returned to agents when retrieval fails. This prevents agents from proceeding with unchecked assumptions.",
+        "affected_ids": [
+          "agent_004",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 9127,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_0.json\",\n  \"title\": \"Algorithm Sample 0: How much did I save by purchasing a season pass in...\",\n  \"description\": \"Multi-agent collaboration sample from algorithm-generated dataset. Agents: ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert. Question: How much did I save by purchasing a season pass instead of daily tickets for California's Great Amer...\",\n  \"trace_type\": \"multi_agent_collaboration\",\n  \"trace_source\": \"al..."
+}

extraction_analysis/cot_extraction_20250907_184217_0ea8fa92.json

@@ -0,0 +1,295 @@
+{
+  "timestamp": "20250907_184217",
+  "extraction_id": "0ea8fa92",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count distinct agents, identify domain and main goal, decide task structure.",
+      "output": "Distinct agents found: 4 (Computer_terminal, DataVerification_Expert, Eateries_Expert, Location-Based_Services_Expert). Domain: location-based services (finding nearby eateries). Main goal: single unified verification/inquiry: determine the closest eatery to Harkness Memorial State Park that is open at 11 PM on Wednesdays. Decision: UNIFIED GOAL -> model as one consolidated Task with multiple specialist agents collaborating."
+    },
+    {
+      "explanation": "2) Entity extraction following priority rules (Agents *_Expert, Tools Computer_*, Inputs/Outputs/Humans, Tasks).",
+      "output": "Agents extracted: agent_001 DataVerification_Expert (HIGH), agent_002 Eateries_Expert (HIGH), agent_003 Location-Based_Services_Expert (HIGH). Tool: tool_001 Computer_terminal (MEDIUM). Input: input_001 user question (HIGH). Output: output_001 eatery result (HIGH). Human: human_001 Requester/Manager (HIGH). Task (consolidated): task_001 Find closest eatery open at 11pm Wed near Harkness Memorial State Park (HIGH)."
+    },
+    {
+      "explanation": "3) Relation mapping (PERFORMS, USES, CONSUMED_BY, PRODUCES, DELIVERS_TO). For unified goal, map multiple agents -> one task and task -> tool, input -> task -> output -> human.",
+      "output": "Mapped relations: PERFORMS (agent_003, agent_002, agent_001) -> task_001; USES task_001 -> tool_001; CONSUMED_BY input_001 -> task_001; PRODUCES task_001 -> output_001; DELIVERS_TO output_001 -> human_001."
+    },
+    {
+      "explanation": "4) Quality check and risk identification: verify id references exist, surface observed failures from trace, and list optimizations.",
+      "output": "All relation IDs reference existing entities. Failures detected from trace metadata and execution logs: (1) DataVerification_Expert produced incorrect Python code causing a mistake (metadata 'mistake_agent' and 'mistake_reason'); (2) perform_web_search returned None causing a TypeError during execution (execution failure). Recommended optimizations: refine prompts for DataVerification_Expert to include error handling and None-checking for web search results; consider merging Location-Based_Services_Expert and Eateries_Expert to reduce handoffs or clarify responsibilities; improve the perform_web_search tool (robust return values/fallbacks)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Harkness Eatery Location + Verification Multi-Agent System",
+    "system_summary": "A location-based multi-agent workflow that finds and verifies the closest eatery to Harkness Memorial State Park that is open at 11 PM on Wednesdays. The system uses three specialist agents (location search, eatery extraction, and data verification) collaborating through a computer terminal tool to consume the user query and produce a verified eatery result for the requester.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataVerification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Eateries_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Location-Based_Services_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Find closest eatery open at 11pm on Wednesdays near Harkness Memorial State Park",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User question: closest eatery to Harkness Memorial State Park open at 11pm on Wednesdays",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified eatery result (name, address, distance, confirmation of being open at 11pm on Wednesdays)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Requester / Manager",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "relation_001",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "relation_002",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "relation_003",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "relation_004",
+        "source": "task_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "relation_005",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "relation_006",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "relation_007",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "AGENT_ERROR",
+        "description": "DataVerification_Expert produced incorrect Python code leading to an incorrect verification step (metadata indicates mistake_agent and mistake_reason).",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "perform_web_search returned None during execution, causing a TypeError and preventing successful extraction of operating hours for some queries.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "tool_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine the DataVerification_Expert prompt to require explicit error handling for web searches (check for None or empty results), add retry/backoff logic, and include a manual fallback verification step when automated searches fail. This reduces AGENT_ERROR and RETRIEVAL_ERROR risk.",
+        "affected_ids": [
+          "agent_001",
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "AGENT_MERGING",
+        "description": "Consider merging Location-Based_Services_Expert and Eateries_Expert into a single LocalSearch_Expert (or define clearer boundaries) to reduce handoffs and duplicated searches; alternatively, add an explicit handoff protocol with shared structured data (locations + hours) to minimize parsing mismatches.",
+        "affected_ids": [
+          "agent_002",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 29909,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_1.json\",\n  \"title\": \"Algorithm Sample 1: What is the closest eatery to Harkness Memorial State Park t...\",\n  \"description\": \"Complex location-based services sample with 4 specialized agents. Involves geographic queries, time-based filtering, and data verification.\",\n  \"trace_type\": \"location_based_services\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"location_services\",\n    \"data_verification\",\n    \"re..."
+}

extraction_analysis/cot_extraction_20250907_184314_fbb41ddf.json

@@ -0,0 +1,298 @@
+{
+  "timestamp": "20250907_184314",
+  "extraction_id": "fbb41ddf",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "JSON structure analysis: count distinct agents, identify domain and main goal, and decide task structure.",
+      "output": "Found 4 distinct agents (Computer_terminal, Probability_Expert, TheoreticalChemistry_Expert, Verification_Expert). Domain: probability/game-theory simulation with cross-disciplinary verification. Main goal: single unified verification/selection task — determine which of 100 balls maximizes ejection probability (single consolidated task)."
+    },
+    {
+      "explanation": "Entity extraction guided by priorities: identify high-priority Agents, Task, Inputs/Outputs, Tools, and Human stakeholders.",
+      "output": "Agents (3 domain experts) and 1 tool (Computer_terminal). One consolidated Task for simulation & verification. Input: riddle/game description. Output: chosen ball number (2). Human stakeholder: contestant/manager receiving the result."
+    },
+    {
+      "explanation": "Relation mapping: map PERFORMS, USES, CONSUMED_BY, PRODUCES, DELIVERS_TO according to workflow.",
+      "output": "All three Expert agents PERFORM the single Task. Task USES the Computer_terminal tool. Input is CONSUMED_BY the Task. Task PRODUCES the Output. Output DELIVERS_TO the Human stakeholder."
+    },
+    {
+      "explanation": "Quality check and risk identification: ensure all IDs reference existing entities, include failure events and actionable optimizations.",
+      "output": "All relation IDs map to defined entities. Two failures identified (execution error in simulation; insufficient validation/confirmation bias). Two optimization recommendations provided (prompt/test refinement; add explicit verification step / tighten simulation validation)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Pick That Ping-Pong — Simulation & Verification Agent System",
+    "system_summary": "Cross-disciplinary agent collaboration to simulate a 100-ball ejection game and verify which ball maximizes ejection probability. Three domain experts (Probability, Theoretical Chemistry, Verification) collaborate using a Computer_terminal tool to run Monte Carlo simulations and confirm results; the system outputs the recommended ball number for a human contestant.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Probability_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "TheoreticalChemistry_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Simulation-based selection: identify ball with highest ejection probability",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Game description and initial ramp/platform state (100 balls, platform starts with 1,2,3)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Recommended ball number to pick (max ejection probability)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Contestant / Manager (recipient of recommendation)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Simulation implementation error introduced incorrect outcome (metadata indicates a mistake by Probability_Expert).",
+        "raw_text": "The agent made an error in the simulation implementation, resulting in an incorrect outcome.",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Insufficient independent validation steps and rapid team consensus risk confirmation bias.",
+        "raw_text": "Verification step acknowledged but consensus reached without independent re-implementation checks.",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_003"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Add an explicit verification checklist in the task prompt requiring independent re-implementation of the simulation, deterministic unit tests for mechanics (position updates for each piston action), and seed-controlled runs to reproduce results before accepting final output.",
+        "affected_ids": [
+          "task_001",
+          "agent_001",
+          "agent_002",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Introduce an explicit Verification subtask (independent re-run & analytical check) or merge verification responsibilities into a reproducible pipeline step to reduce human confirmation bias; require cross-check by at least two independent agents/tools.",
+        "affected_ids": [
+          "task_001",
+          "agent_003",
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 16685,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_3.json\",\n  \"title\": \"Algorithm Sample 3: Here's a fun riddle that I think you'll enjoy.\\n\\nYou have bee...\",\n  \"description\": \"Cross-disciplinary collaboration between probability and theoretical chemistry experts solving complex riddle scenarios\",\n  \"trace_type\": \"probability_game_theory\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"probability\",\n    \"theoretical_chemistry\",\n    \"game_theory\",\n    \"sim..."
+}

extraction_analysis/cot_extraction_20250907_184425_646f3115.json

@@ -0,0 +1,210 @@
+{
+  "timestamp": "20250907_184425",
+  "extraction_id": "646f3115",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count distinct agents, identify domain and main goal, decide task structure.",
+      "output": "Detected 1 distinct agent (trace_metadata.agent_count = 1). Domain: programming / documentation (Python). Main goal: single-user inquiry answered via retrieval + LLM synthesis — a UNIFIED GOAL (one consolidated task): answer the user's question about Python list comprehensions with explanation and examples."
+    },
+    {
+      "explanation": "2) Entity extraction: identify Agents (specialized), Tasks, Tools, Inputs/Outputs, Human stakeholders.",
+      "output": "Entities chosen: agent_001 (Python Programming Assistant), task_001 (Answer Python List Comprehension Inquiry), tool_001 (Documentation Search Service), tool_002 (LLM model: gpt-4o-2024-11-20), input_001 (User question), output_001 (Explanation of list comprehensions), output_002 (Practical code examples & performance claim), human_001 (Learner/demo-user-001)."
+    },
+    {
+      "explanation": "3) Relation mapping: map PERFORMS, USES, PRODUCES, DELIVERS_TO, CONSUMED_BY according to the unified workflow pattern.",
+      "output": "Mapped relations: agent_001 PERFORMS task_001; agent_001 USES tool_001 and tool_002; input_001 CONSUMED_BY task_001; task_001 PRODUCES output_001 and output_002; outputs DELIVERED_TO human_001. All relations reference existing entity IDs."
+    },
+    {
+      "explanation": "4) Quality check: validate references, ensure workflow Input→Agent→Task→Output→Human, surface failures and optimizations.",
+      "output": "Workflow is complete: input_001 → agent_001 → task_001 → {output_001, output_002} → human_001. Two risk events identified (hallucination risk in a performance claim; missing agent identity in component_hierarchy). Two optimization recommendations provided (prompt refinement and search/tool enhancement)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Python Documentation Assistant (RAG demo)",
+    "system_summary": "A single Python programming assistant that answers beginner questions about Python list comprehensions by searching documentation and synthesizing concise explanations and code examples using an LLM. The workflow is a unified inquiry-response: user question → retrieval + inference → explanation & examples → delivered to learner.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Python Programming Assistant",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Answer Python List Comprehension Inquiry",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Documentation Search Service",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_002",
+        "type": "Tool",
+        "name": "LLM model: gpt-4o-2024-11-20",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User question about list comprehensions",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Explanation of list comprehensions (uses, when to use, syntax)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_002",
+        "type": "Output",
+        "name": "Practical code examples and performance claim",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Learner (beginner) - demo-user-001",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_001",
+        "target": "tool_002",
+        "type": "USES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_004",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_006",
+        "source": "task_001",
+        "target": "output_002",
+        "type": "PRODUCES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_007",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_008",
+        "source": "output_002",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "HALLUCINATION",
+        "description": "Performance claim (\"20-30% faster\") presented without citation — potential unsupported or context-dependent claim.",
+        "raw_text": "List comprehensions are not only more concise but also typically 20-30% faster than equivalent for loops!",
+        "raw_text_ref": [],
+        "affected_id": "output_002"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "AGENT_ERROR",
+        "description": "Agent identity is not explicitly recorded in component_hierarchy (empty agent name in trace metadata), which can cause attribution/tracing gaps.",
+        "raw_text": "component_hierarchy\": { \"agents\": [ \"\" ]",
+        "raw_text_ref": [],
+        "affected_id": "agent_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Add explicit citation and confidence scaffolding to the assistant system prompt and final responses (e.g., include source links and a short confidence statement). This reduces hallucination risk for performance claims and improves verifiability.",
+        "affected_ids": [
+          "agent_001",
+          "task_001",
+          "output_002"
+        ],
+        "raw_text_ref": []
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enhance the Documentation Search Service to return structured provenance (document titles + URLs + snippet ranks) and make provenance mandatory in the retrieval output so the assistant can cite exact sources.",
+        "affected_ids": [
+          "tool_001",
+          "tool_002"
+        ],
+        "raw_text_ref": []
+      }
+    ]
+  },
+  "input_trace_length": 10504,
+  "input_trace_preview": "{\n  \"filename\": \"python_documentation_inquiry.json\",\n  \"title\": \"Python Documentation Assistant Demo\",\n  \"description\": \"Comprehensive example showing RAG-powered AI assistant handling multi-turn programming inquiry with knowledge search, detailed explanations, code examples, performance analysis, and interactive learning\",\n  \"trace_type\": \"documentation_search\",\n  \"trace_source\": \"sample_data\",\n  \"tags\": [\n    \"programming\",\n    \"rag_assistant\",\n    \"documentation\",\n    \"failure_detection\",\n   ..."
+}

improvement_implementation_log_20250907_162939.txt

@@ -0,0 +1,70 @@
+
+=== TECHNICAL IMPROVEMENT IMPLEMENTATION SUMMARY ===
+Date: Sun Sep  7 16:29:39 BST 2025
+Project: OpenAI CoT Knowledge Graph Extractor Enhancement
+
+COMPLETED IMPROVEMENTS:
+========================
+
+1. ✅ Few-Shot Example Replacement
+   - Replaced simple Sample 0 with complex Sample 1 (3-agent, 3-task workflow)
+   - Updated system_name to 'Location-Based Restaurant Discovery System' 
+   - Added 2 NEXT relations and 3 PERFORMS relations in example
+   - Result: No measurable impact due to input parsing issues
+
+2. ✅ Relation Type Prioritization  
+   - Moved PERFORMS and NEXT to top of relation types list
+   - Added 'HIGH PRIORITY' annotations and 'LIMIT USE' warnings
+   - Reduced priority of ASSIGNED_TO and INTERVENES
+   - Result: Minimal impact on relation type selection
+
+3. ✅ Task Decomposition Guidelines
+   - Added explicit 'TASK DECOMPOSITION GUIDELINES' section
+   - Specified 1:1 agent-task mapping for complex workflows (3+ agents)
+   - Defined sequential flow requirements using NEXT relations
+   - Result: Guidelines ignored due to conflicting patterns
+
+4. ✅ Workflow Complexity Detection
+   - Added 'WORKFLOW COMPLEXITY DETECTION' section
+   - Defined SIMPLE (1-2 agents) vs COMPLEX (3+ agents) criteria
+   - Updated CRITICAL RELATIONSHIP PATTERNS for both scenarios
+   - Result: Detection failed due to incorrect agent counting
+
+5. ✅ CoT Reasoning Enhancement
+   - Made workflow complexity analysis mandatory first step
+   - Added explicit agent counting and classification requirements
+   - Enforced task decomposition analysis in reasoning chain
+   - Result: CoT steps added but based on incorrect input parsing
+
+ROOT CAUSE ANALYSIS:
+===================
+❌ CRITICAL ISSUE: JSON trace parsing failure
+   - Model only detects 1 agent instead of 3+ agents in complex traces
+   - All samples misidentified as 'Customer Data' domain regardless of content
+   - Complex workflow detection impossible with incorrect agent counts
+   - All prompt engineering improvements rendered ineffective
+
+TECHNICAL CHANGES MADE:
+======================
+File: agentgraph/methods/production/openai_structured_extractor.py
+- Lines 115-321: Replaced few-shot example with Sample 1 structure
+- Lines 333-343: Reordered and prioritized relation types  
+- Lines 374-387: Added task decomposition guidelines
+- Lines 374-378: Added workflow complexity detection criteria
+- Lines 389-411: Updated relationship patterns for simple/complex workflows
+- Lines 109-122: Enhanced CoT guidance with mandatory complexity analysis
+
+BACKUP CREATED:
+==============
+openai_structured_extractor_backup_20250907_162939.py
+
+NEXT REQUIRED ACTIONS:
+=====================
+1. 🔧 Fix JSON trace parsing to correctly identify all agents from observations array
+2. 🔧 Improve content analysis beyond surface-level keyword detection  
+3. 🧪 Re-test all improvements after fixing input processing
+4. 📊 Verify workflow complexity detection with correct agent identification
+5. ✅ Validate final improvement effectiveness against gold standards
+
+STATUS: Improvements implemented but effectiveness blocked by input parsing issues.
+