add

agentgraph/methods/production/openai_structured_extractor.py

@@ -76,11 +76,11 @@ ENTITY TYPES & PRIORITIES:
 - 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
+RELATION PRIORITIES (ULTRA-SIMPLIFIED):
+- PERFORMS (Agent→Task): ONLY agent-task relation needed
+- Input→Agent→Task→Output→Human: Essential workflow chain
+- NO COMPLEX RELATIONS: Avoid ASSIGNED_TO, INTERVENES, REQUIRED_BY
+- TARGET: 6-8 total relations maximum (keep it simple!)
 
 WORKFLOW PATTERNS:
 - Simple (1-2 agents): Single consolidated task, basic relations
@@ -184,22 +184,29 @@ ANALYSIS STEPS:
    * Clear responsibility boundaries prevent "全连接混乱"  
    * Parallel task execution improves transparency and efficiency
    
-   MANDATORY RULE: NO TASK SHARING
-   * NEVER assign multiple agents to the same task
+   MANDATORY RULE: NO TASK SHARING - ABSOLUTELY FORBIDDEN!
+   * NEVER EVER assign multiple agents to the same task
    * Each task must have exactly ONE agent performing it
-   * Use task decomposition instead of agent collaboration on single tasks
-
-4. RELATION MAPPING (Strict 1:1 Task Assignment):
-   - PERFORMS: EXACTLY one agent per task (no sharing, no collaboration on same task)
-     * VERIFICATION: agent_001→task_001, agent_002→task_002, agent_003→task_003
-     * DISCOVERY: agent_001→task_001, agent_002→task_002, agent_003→task_003  
-     * INTERDISCIPLINARY: agent_001→task_001, agent_002→task_002, agent_003→task_003
-     * SIMPLE: agent_001→task_001
+   * If you see 3 agents, you MUST create 3 separate tasks
+   * Task sharing = IMMEDIATE FAILURE - completely unacceptable
+   * ALWAYS decompose into independent subtasks for each agent
+
+4. RELATION MAPPING (FORCED 1:1 MAPPING):
+   - PERFORMS: Each agent performs EXACTLY ONE UNIQUE task
+     * 3 agents = 3 different tasks = 3 PERFORMS relations
+     * agent_001 → task_001, agent_002 → task_002, agent_003 → task_003
+     * NEVER: agent_001 → task_001, agent_002 → task_001 (FORBIDDEN!)
+     * NO other agent-task relations (ASSIGNED_TO, INTERVENES, etc.)
    
-   - NEXT: Sequential task dependencies (task_001→task_002→task_003)
-   - CONSUMED_BY/PRODUCES/DELIVERS_TO: Standard workflow flow
-   - USES/REQUIRED_BY: Tool and support connections only
-   - ABSOLUTE RULE: Each task has EXACTLY ONE performer - no exceptions!
+   - MINIMAL ESSENTIAL RELATIONS:
+     * Input→Agent (CONSUMED_BY): 1 relation only
+     * Task→Task (NEXT): For sequential workflows only
+     * Last Task→Output (PRODUCES): 1 relation only  
+     * Output→Human (DELIVERS_TO): 1 relation only
+     * Agent→Tool (USES): Only if tools exist
+   
+   - FORBIDDEN: ASSIGNED_TO, INTERVENES, REQUIRED_BY, complex multi-connections
+   - TARGET: Maximum 9 total relations for 3-agent workflows
 
 5. QUALITY CHECK (Contextual Graph Enhanced):
    - Verify all relation IDs reference existing entities

extraction_analysis/cot_extraction_20250907_201813_640d987c.json

@@ -0,0 +1,370 @@
+{
+  "timestamp": "20250907_201813",
+  "extraction_id": "640d987c",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: identify distinct agents and domain.",
+      "output": "Detected 4 listed components in content.data.agents: Computer_terminal, DataAnalysis_Expert, DataVerification_Expert, StatisticalAnalysis_Expert. Domain: wildlife_data_analysis / ecological statistical analysis. Main goal: verify and report the count of nonindigenous crocodiles found in Florida between 2000 and 2020 (a verification-style statistical counting task)."
+    },
+    {
+      "explanation": "2) Decide workflow pattern and task structure using Gold-standard mapping and provided rules.",
+      "output": "Trace contains explicit verification steps and count objective plus three specialized experts. Map to VERIFICATION workflow (3 specialized tasks) so that each expert is assigned exactly one independent task (1:1 mapping). Computer_terminal is classified as a Tool."
+    },
+    {
+      "explanation": "3) Extract entities (Agents, Tasks, Tool, Input, Output, Human) and produce unique IDs.",
+      "output": "Agents: DataAnalysis_Expert (agent_001), StatisticalAnalysis_Expert (agent_002), DataVerification_Expert (agent_003). Tool: Computer_terminal (tool_001). Tasks (one per expert): task_001 Dataset Confirmation & Acquisition, task_002 Data Extraction & Statistical Counting, task_003 Verification & Reporting. Input: Research Question (input_001). Output: Final Count (output_001). Human stakeholder: Manager / End User (human_001)."
+    },
+    {
+      "explanation": "4) Map relations with strict 1:1 task assignment and sequential dependencies.",
+      "output": "Input consumed by DataAnalysis_Expert -> agent_001 performs task_001 -> NEXT task_001 -> task_002 -> NEXT task_002 -> task_003 -> task_003 produces the final output delivered to the human. Agents 1 and 2 use the Computer_terminal tool (USES)."
+    },
+    {
+      "explanation": "5) Identify failures observed in trace and recommend optimizations.",
+      "output": "Failures: (a) DataAnalysis_Expert failed to locate the correct dataset URL (trace metadata 'mistake_agent'). (b) Downloaded file was HTML (placeholder) causing CSV parsing error (pandas ParserError). Optimizations: add URL/content-type validation and download verification, and strengthen explicit manager instruction to require content-type/checksum and a pre-download URL-confirmation step."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Wildlife Statistical Verification System (Crocodile Count)",
+    "system_summary": "Multi-agent verification workflow to determine and validate the count of nonindigenous crocodiles in Florida (2000–2020). Three specialized experts handle dataset acquisition, statistical extraction/counting, and verification/reporting, supported by a Computer Terminal tool.",
+    "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": "StatisticalAnalysis_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": "task_001",
+        "type": "Task",
+        "name": "Dataset Confirmation & Acquisition",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Data Extraction & Statistical Counting",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Verification & Reporting",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Research Question: Count nonindigenous crocodiles in Florida (2000-2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Final Count of nonindigenous crocodiles (Florida, 2000-2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Manager / End User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_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_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_010",
+        "source": "agent_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "AGENT_ERROR",
+        "description": "DataAnalysis_Expert failed to locate/confirm the correct USGS dataset URL (metadata indicates mistake_agent).",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Downloaded placeholder file was HTML (Example Domain), causing a CSV parsing error when attempting to read the dataset.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "tool_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add pre-download validation in the acquisition task: check URL resolution, HTTP status, Content-Type, and basic file magic bytes before saving as CSV. This prevents HTML pages being saved as CSV.",
+        "affected_ids": [
+          "task_001",
+          "tool_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine manager instructions and agent prompts to require explicit URL confirmation and a checksum/content-type assertion step. Include a mandatory 'verify URL and sample lines' substep before parsing.",
+        "affected_ids": [
+          "task_001",
+          "task_003",
+          "agent_001",
+          "agent_003"
+        ],
+        "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_202019_0e78d29a.json

@@ -0,0 +1,220 @@
+{
+  "timestamp": "20250907_202019",
+  "extraction_id": "0e78d29a",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count distinct agents, identify domain and main goal.",
+      "output": "Distinct agents (excluding Computer_* tools): DataAnalysis_Expert, DataVerification_Expert, StatisticalAnalysis_Expert (3 agents). Domain: ecological / wildlife statistical analysis. Main goal: verify and compute the number of nonindigenous crocodiles found in Florida from 2000–2020 using USGS NAS dataset (a data-extraction + verification task)."
+    },
+    {
+      "explanation": "2) Entity extraction following naming patterns and priority rules.",
+      "output": "Agents extracted: DataAnalysis_Expert, DataVerification_Expert, StatisticalAnalysis_Expert (HIGH). Tool extracted: Computer_terminal (MEDIUM). Input: user query (HIGH). Output: numeric result (HIGH). Human stakeholder: Manager / End User (HIGH)."
+    },
+    {
+      "explanation": "3) Workflow classification & task generation using mandatory rule (each agent = one task) and task templates for data-analysis/verification workflows.",
+      "output": "Workflow type: SIMPLE VERIFICATION / DATA ANALYSIS (multi-agent). Tasks generated (1:1 agent→task): task_001 Dataset Acquisition & Inspection (DataAnalysis_Expert), task_002 URL & File Validation (DataVerification_Expert), task_003 Count Extraction & Statistical Analysis (StatisticalAnalysis_Expert)."
+    },
+    {
+      "explanation": "4) Relation mapping (ultra-simplified): create minimal relations to form Input→Agent→Task→Output→Human chain plus tool usage and agent-task PERFORMS.",
+      "output": "Relations created: input_001 CONSUMED_BY agent_001; agent_001 PERFORMS task_001; agent_002 PERFORMS task_002; agent_003 PERFORMS task_003; task_003 PRODUCES output_001; output_001 DELIVERS_TO human_001; agent_001 USES tool_001. Total relations: 7 (within target)."
+    },
+    {
+      "explanation": "5) Quality check and risk identification: verify entity/relation references and extract failures/optimizations from trace metadata and observations.",
+      "output": "Two failures identified (metadata: mistake_agent DataAnalysis_Expert; parser error showing HTML content). Two optimizations recommended (URL verification & file-type checks; add download validation/caching)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Ecological Statistical Analysis for Invasive Species Count",
+    "system_summary": "Multi-agent data-analysis workflow to obtain and verify counts of nonindigenous crocodiles in Florida (2000–2020) using the USGS Nonindigenous Aquatic Species dataset. The system separates responsibilities across dataset acquisition, URL/file validation, and statistical extraction, with a Computer terminal tool used for downloads and file inspection.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataAnalysis_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "DataVerification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "StatisticalAnalysis_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": "Dataset Acquisition & Inspection",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "URL & File Validation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Count Extraction & Statistical Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Query: Count of nonindigenous crocodiles in Florida (2000–2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Nonindigenous Crocodile Count (Florida, 2000–2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Manager / End User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_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": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_005",
+        "source": "task_003",
+        "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": []
+      },
+      {
+        "id": "rel_007",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "DataAnalysis_Expert failed to locate/confirm the correct dataset URL, leading to an incorrect (placeholder) download and ultimately an incorrect analysis attempt (metadata indicates mistake_agent).",
+        "raw_text": "metadata.mistake_reason: The agent failed to locate the correct URL for the dataset from the USGS Nonindigenous Aquatic Species database.",
+        "raw_text_ref": [],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Downloaded file was an HTML page (Example Domain) not a CSV; pandas parsing failed with 'Error tokenizing data' causing data-extraction to fail.",
+        "raw_text": "pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 8, saw 2; head output showed an HTML document.",
+        "raw_text_ref": [],
+        "affected_id": "tool_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Add an explicit preliminary URL-validation step owned by DataVerification_Expert before any download attempts (confirm file type, content-disposition, and HTTP 200/CSV Content-Type). This avoids placeholder downloads and reduces rework.",
+        "affected_ids": [
+          "agent_002",
+          "task_002"
+        ],
+        "raw_text_ref": []
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add automated download verification (file-type sniffing, sample head inspection, checksum or schema validation) in the Computer_terminal download workflow so that HTML or error pages are rejected before parsing.",
+        "affected_ids": [
+          "tool_001",
+          "task_001"
+        ],
+        "raw_text_ref": []
+      }
+    ]
+  },
+  "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_202214_e432abe3.json

@@ -0,0 +1,299 @@
+{
+  "timestamp": "20250907_202214",
+  "extraction_id": "e432abe3",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) Count distinct agents and identify domain/goal from trace.",
+      "output": "Agents detected in data.agents and component_hierarchy: Computer_terminal, DataAnalysis_Expert, DataVerification_Expert, StatisticalAnalysis_Expert (4 distinct agents). Domain: ecological / wildlife data analysis. Main goal: verify and report the number of nonindigenous crocodiles found in Florida from 2000 through 2020 using USGS NAS dataset (single verification/analysis objective)."
+    },
+    {
+      "explanation": "2) Decide workflow pattern using Gold standard patterns and trace cues.",
+      "output": "The trace contains a single verification-style question (explicit instruction to extract and verify a count) and multiple specialized experts collaborating. This maps to the SIMPLE VERIFICATION pattern (1 consolidated task with multiple experts collaborating)."
+    },
+    {
+      "explanation": "3) Extract entities (Agents, Tools, Task, Input, Output, Human) following extraction rules.",
+      "output": "Agents: DataAnalysis_Expert, DataVerification_Expert, StatisticalAnalysis_Expert (HIGH). Tool: Computer_terminal (MEDIUM). Task: one consolidated verification task 'Nonindigenous Crocodile Count Verification' (HIGH). Input: user question (HIGH). Output: numeric result (HIGH). Human: End User (HIGH)."
+    },
+    {
+      "explanation": "4) Create minimal relation set consistent with rules: Input→Agent (CONSUMED_BY), Agent→Task (PERFORMS) for each agent, Task→Output (PRODUCES), Output→Human (DELIVERS_TO), and Tool usage (USES) since Computer_terminal was used.",
+      "output": "Relations created: input→DataAnalysis_Expert (CONSUMED_BY), DataAnalysis_Expert→task (PERFORMS), DataVerification_Expert→task (PERFORMS), StatisticalAnalysis_Expert→task (PERFORMS), task→output (PRODUCES), output→human (DELIVERS_TO), DataAnalysis_Expert→Computer_terminal (USES). Total relations = 7 (within limit)."
+    },
+    {
+      "explanation": "5) Identify failures and optimizations from trace evidence (metadata and logged errors).",
+      "output": "Failures: (a) DataAnalysis_Expert failed to find the correct dataset URL (metadata mistake_agent). (b) Downloaded placeholder file contained HTML leading to CSV parse failure (pandas ParserError). Optimizations: (a) add automated URL verification / discovery and retry; (b) add download validation (Content-Type, small line preview) before parsing."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Wildlife Ecological Data Verification System",
+    "system_summary": "Multi-expert system to locate, download, extract, analyze and verify counts of invasive species from government datasets (USGS NAS). The workflow is a single verification/analysis task executed collaboratively by DataAnalysis, DataVerification, and StatisticalAnalysis experts supported by a Computer terminal tool.",
+    "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": "task_001",
+        "type": "Task",
+        "name": "Nonindigenous Crocodile Count Verification (Florida, 2000-2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Question: Count nonindigenous crocodiles in Florida (2000-2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified count of nonindigenous crocodiles in Florida (2000-2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "End User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_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": "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": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "DataAnalysis_Expert failed to locate or confirm the correct dataset URL from the USGS NAS; placeholder URL was used.",
+        "raw_text": "metadata.mistake_agent: DataAnalysis_Expert; mistake_reason: The agent failed to locate the correct URL for the dataset from the USGS Nonindigenous Aquatic Species database.",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Downloaded file was an HTML placeholder (Example Domain) causing pandas CSV parsing error.",
+        "raw_text": "pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 8, saw 2; earlier head output shows '<!doctype html> ...' indicating an HTML page was saved instead of CSV.",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "tool_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Validate downloads immediately after retrieval: check HTTP status, Content-Type header, and preview first N lines to ensure CSV format before saving/processing. Implement automatic retry/backoff and host resolution checks when curl fails.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Add an explicit URL-discovery and confirmation step to the plan (e.g., locate canonical download link on NAS site, confirm with DataVerification_Expert, then download). Include checksum/content-preview verification and an explicit verification task to prevent using placeholder URLs.",
+        "affected_ids": [
+          "agent_001",
+          "agent_002"
+        ],
+        "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_202426_1c970c01.json

@@ -0,0 +1,354 @@
+{
+  "timestamp": "20250907_202426",
+  "extraction_id": "1c970c01",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "JSON structure analysis: count distinct agents and identify domain and main goal.",
+      "output": "Detected 3 expert agents (DataAnalysis_Expert, DataVerification_Expert, StatisticalAnalysis_Expert) plus one Computer_terminal (tool). Domain: wildlife_data_analysis. Main goal: determine the number of nonindigenous crocodiles found in Florida from 2000–2020 (a sequential data-acquisition → extraction → verification workflow)."
+    },
+    {
+      "explanation": "Decide workflow type and task decomposition using gold-standard patterns and developer constraints.",
+      "output": "Selected COMPLEX SEQUENTIAL WORKFLOW (3 high-level tasks). Map 3 expert agents to 3 distinct tasks (1:1) and treat Computer_terminal as a Tool."
+    },
+    {
+      "explanation": "Extract entities (Agents, Tasks, Tool, Input, Output, Human) and prepare relation mapping following mandatory relation rules (single CONSUMED_BY, PERFORMS 1:1, NEXT chain, PRODUCES, DELIVERS_TO, optional USES).",
+      "output": "Created 3 agent entities, 3 tasks, 1 tool, 1 input, 1 output, 1 human; mapped relations: input→agent, each agent PERFORMS unique task, task NEXT chain, last task PRODUCES output, output DELIVERS_TO human, one agent USES Computer_terminal."
+    },
+    {
+      "explanation": "Locate failures and create optimizations based on trace evidence (metadata mistake_agent and observed HTML/download/parsing errors).",
+      "output": "Recorded a RETRIEVAL_ERROR where DataAnalysis_Expert failed to locate correct URL and a CSV download produced HTML; proposed two optimizations: URL validation & centralized retrieval logic, and improved retry/logging mechanisms for dataset download."
+    },
+    {
+      "explanation": "Quality checks: ensure all relation IDs reference existing entities and the workflow chain is complete Input→Agent→Task→Output→Human.",
+      "output": "All references validated; preserved empty raw_prompt and interaction_prompt fields per formatting rules."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Wildlife Dataset Verification and Counting System",
+    "system_summary": "Multi-agent sequential workflow to acquire a USGS invasive-species dataset, extract/count records for nonindigenous crocodiles in Florida (2000–2020), and verify results. Three expert agents perform acquisition, extraction/counting, and verification/reporting, supported by a Computer terminal tool.",
+    "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": "task_001",
+        "type": "Task",
+        "name": "Dataset Acquisition (confirm URL & download)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Data Extraction and Counting (filter Florida crocodile records 2000–2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Verification and Reporting (validate counts & produce final result)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Manager Query: Count nonindigenous crocodiles in Florida (2000–2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified count of nonindigenous crocodiles in Florida (2000–2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Project Manager / End User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_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_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "DataAnalysis_Expert failed to locate the correct URL; placeholder download produced an HTML page instead of the CSV, causing parsing failures.",
+        "raw_text": "The agent failed to locate the correct URL for the dataset from the USGS Nonindigenous Aquatic Species database.",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Attempt to parse the downloaded file raised a pandas ParserError due to HTML content in place of CSV.",
+        "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": "WORKFLOW_SIMPLIFICATION",
+        "description": "Centralize dataset URL discovery and validation in the DataVerification_Expert stage: verify content-type and sample lines before accepting a download (reject HTML responses), and only then hand off to DataAnalysis_Expert for parsing.",
+        "affected_ids": [
+          "agent_002",
+          "agent_001",
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add automatic URL resolution, retry, and content-type checks to download commands (e.g., curl with --fail and HEAD requests) and log the resolved URL; implement automated sample-line checks before invoking pandas.read_csv.",
+        "affected_ids": [
+          "task_001",
+          "tool_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_202629_0acb1b2e.json

@@ -0,0 +1,395 @@
+{
+  "timestamp": "20250907_202629",
+  "extraction_id": "0acb1b2e",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Count distinct agents and identify domain & main goal.",
+      "output": "Detected 4 named actors in data.agents but one (Computer_terminal) is a tool. Three specialist agents (DataAnalysis_Expert, DataVerification_Expert, StatisticalAnalysis_Expert). Domain: wildlife/ecological statistical analysis. Main goal: determine the number of nonindigenous crocodiles found in Florida from 2000 through 2020 (a verification/counting task based on an external dataset)."
+    },
+    {
+      "explanation": "Decide workflow pattern and task decomposition following the mandatory 1-agent→1-task rule.",
+      "output": "Workflow classed as a VERIFICATION / DATA_ANALYSIS process. Because there are 3 specialist agents, create 3 distinct tasks (one per agent). Computer_terminal is modeled as a Tool. Tasks are arranged sequentially according to the plan observed in the trace (acquisition → verification → statistical counting)."
+    },
+    {
+      "explanation": "Extract entities (agents, tool, tasks, I/O, human) and map minimal relations consistent with the trace.",
+      "output": "Created entities: 3 Agent entities, 1 Tool, 3 Tasks, 1 Input, 1 Output, 1 Human. Created PERFORMS relations (one per agent→task), an Input CONSUMED_BY relation, Task NEXT sequence relations, final PRODUCES and DELIVERS_TO relations, and a single USES relation for the tool."
+    },
+    {
+      "explanation": "Identify failures and propose optimizations based on trace evidence (parser error, placeholder URL).",
+      "output": "Two failures recorded: incorrect URL/placeholder download (retrieval failure by DataAnalysis_Expert) and CSV parsing/execution failure due to HTML file (execution error affecting acquisition task). Two optimizations suggested: add URL/content-type validation and a prompt/workflow step to confirm dataset source and use HEAD requests or API endpoints before download."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "USGS Nonindigenous Species Counting & Verification System",
+    "system_summary": "Multi-agent system for counting nonindigenous crocodiles in Florida (2000–2020). Three specialist agents collaborate in a sequential verification workflow: data acquisition & exploration, dataset verification/integrity checking, and statistical counting & interpretation. A Computer_terminal tool is used for downloads and file inspection.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataAnalysis_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 9,
+            "line_end": 9
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "DataVerification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 6
+          },
+          {
+            "line_start": 8,
+            "line_end": 8
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "StatisticalAnalysis_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "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
+          },
+          {
+            "line_start": 7,
+            "line_end": 7
+          },
+          {
+            "line_start": 10,
+            "line_end": 10
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Data Acquisition & Exploration",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 9,
+            "line_end": 9
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Dataset Verification & Integrity Checking",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Statistical Counting & Interpretation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Analysis Request: Count nonindigenous crocodiles in Florida (2000–2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified count of nonindigenous crocodiles in Florida (2000–2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Requestor / Manager",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 9,
+            "line_end": 9
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 6,
+            "line_end": 9
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 10,
+            "line_end": 10
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "DataAnalysis_Expert used a placeholder/incorrect URL and failed to locate the correct dataset URL, leading to an invalid download.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 9,
+            "line_end": 9
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "CSV parsing failed because the downloaded file was HTML (placeholder webpage), causing parsing and execution errors when exploring the dataset.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add automated URL/content-type validation before attempting to download or parse files (e.g., perform HEAD requests to confirm 'text/csv' content-type, and check HTTP status). This will prevent downloading HTML placeholder pages and reduce parsing failures.",
+        "affected_ids": [
+          "agent_001",
+          "task_001",
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine the agent plan to require explicit confirmation of the authoritative USGS dataset URL (or use the USGS API endpoint) as a mandatory step before data processing; include fallback search logic and checksum/source validation.",
+        "affected_ids": [
+          "agent_002",
+          "agent_001",
+          "task_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 4,
+            "line_end": 6
+          },
+          {
+            "line_start": 9,
+            "line_end": 9
+          }
+        ]
+      }
+    ]
+  },
+  "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_202727_6b876a48.json

@@ -0,0 +1,350 @@
+{
+  "timestamp": "20250907_202727",
+  "extraction_id": "6b876a48",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Parse JSON trace to find distinct agents and domain.",
+      "output": "Found 4 named components in content.data.agents: Computer_terminal, DataVerification_Expert, Eateries_Expert, Location-Based_Services_Expert. Domain is location-based services; main goal is a multi-step discovery/verification: find closest eatery open at 11pm Wednesday."
+    },
+    {
+      "explanation": "Classify workflow pattern and generate tasks per rules.",
+      "output": "Workflow matches DISCOVERY (location-based). Following mandatory 1-agent→1-task mapping for 3 non-Computer agents, produce 3 sequential tasks: Geographic Analysis, Restaurant Data Collection, Operating Hours Verification."
+    },
+    {
+      "explanation": "Extract entities (Agents, Tool, Input, Output, Human, Tasks) and create minimal relation set following the forced mapping rules.",
+      "output": "Created 3 Agent entities, 1 Tool, 3 Tasks, 1 Input, 1 Output, 1 Human; mapped relations: Input→Agent (CONSUMED_BY), each Agent→its Task (PERFORMS), Task→Task NEXT chain, final Task→Output PRODUCES, Output→Human DELIVERS_TO, plus single Agent→Tool USES."
+    },
+    {
+      "explanation": "Identify failures and optimizations from trace metadata and execution logs.",
+      "output": "Detected execution failure in DataVerification_Expert (TypeError from perform_web_search returning None). Recommended improving web-search wrapper and error handling."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Location-Based Restaurant Discovery System",
+    "system_summary": "Sequential multi-agent system to find the closest eatery to a park that is open at a specified time. Location-Based_Services_Expert performs geographic search, Eateries_Expert collects candidate eatery data, DataVerification_Expert verifies operating hours (using a Computer terminal tool).",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Location-Based Services Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 12,
+            "line_end": 14
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Eateries Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 12,
+            "line_end": 14
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Data Verification Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 12,
+            "line_end": 14
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer Terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 12,
+            "line_end": 14
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Geographic Proximity Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 26
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Restaurant Data Collection",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 26,
+            "line_end": 36
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Operating Hours Verification",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 36,
+            "line_end": 48
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Restaurant Query",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 8,
+            "line_end": 8
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Restaurant Recommendation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 40,
+            "line_end": 44
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "End User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 2
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 8,
+            "line_end": 8
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 26
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 26,
+            "line_end": 36
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 36,
+            "line_end": 48
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 26,
+            "line_end": 30
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 34,
+            "line_end": 40
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 44,
+            "line_end": 48
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 40,
+            "line_end": 44
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 52,
+            "line_end": 60
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "DataVerification_Expert execution failed due to a TypeError when perform_web_search returned None (code did not guard for None).",
+        "raw_text": "TypeError: 'NoneType' object is not iterable",
+        "raw_text_ref": [
+          {
+            "line_start": 60,
+            "line_end": 62
+          }
+        ],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Initial searches returned eateries but none verified open until 11 PM on Wednesdays; broader-radius searches still did not locate a qualifying eatery.",
+        "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": 22,
+            "line_end": 24
+          }
+        ],
+        "affected_id": "task_003"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Harden the web-search wrapper (perform_web_search) with explicit None handling, retries, and structured result normalization. Add logging for failed calls and a fallback scraping/parsing path to avoid TypeError when results are missing.",
+        "affected_ids": [
+          "agent_003",
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 58,
+            "line_end": 62
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Cache nearby-eatery search results from the Location-Based Services agent to avoid repeated broad-radius searches; this reduces calls and improves reproducibility of hours verification.",
+        "affected_ids": [
+          "agent_001",
+          "task_001",
+          "task_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 26,
+            "line_end": 36
+          }
+        ]
+      }
+    ]
+  },
+  "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_202836_d053e17c.json

@@ -0,0 +1,339 @@
+{
+  "timestamp": "20250907_202836",
+  "extraction_id": "d053e17c",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: counted distinct agents (exclude Computer_* tools). Located main goal and domain from content/tags/description.",
+      "output": "Detected 3 distinct expert agents (Probability_Expert, TheoreticalChemistry_Expert, Verification_Expert). Domain: cross-disciplinary (probability + theoretical chemistry). Main goal: determine which ping-pong ball (1-100) maximizes ejection probability (verification of simulation-based result)."
+    },
+    {
+      "explanation": "2) Workflow classification and task decomposition following the mandated 1:1 mapping (each agent → unique task) and gold-standard interdisciplinary pattern.",
+      "output": "Classified as INTERDISCIPLINARY_ANALYSIS. Created 3 domain-specific tasks: Statistical Simulation & Analysis, Theoretical Modeling & Interpretation, Result Verification & Reporting."
+    },
+    {
+      "explanation": "3) Entity extraction: created Agent, Task, Tool, Input, Output, Human entities. Marked Computer_terminal as a Tool (Computer* → Tool). Kept raw_prompt fields empty as required and used placeholder references.",
+      "output": "Nine entities (3 agents, 3 tasks, 1 tool, 1 input, 1 output, 1 human)."
+    },
+    {
+      "explanation": "4) Relation mapping under the forced 1:1 mapping rules: single Input→Agent relation, each Agent PERFORMS exactly one Task, sequential NEXT relations between tasks (reflecting sequential multi-agent collaboration in trace), last Task→Output PRODUCES, Output→Human DELIVERS_TO. Omitted Agent→Tool USES to keep relations compact.",
+      "output": "Eight relations (1 CONSUMED_BY, 3 PERFORMS, 2 NEXT, 1 PRODUCES, 1 DELIVERS_TO)."
+    },
+    {
+      "explanation": "5) Quality check, failures and optimizations: referenced trace metadata showing a simulation mistake by Probability_Expert and consensus by Verification_Expert. Produced two failures and two optimization recommendations to reduce recurrence.",
+      "output": "Identified execution error in simulation and oversight in verification; recommended analytical cross-checks and automated testing/logging."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Cross-Disciplinary Ping-Pong Ejection Analysis System",
+    "system_summary": "A three-expert interdisciplinary workflow that uses a simulation (run on a Computer terminal) to estimate ejection probabilities for 100 ping-pong balls. Probability_Expert runs simulations, TheoreticalChemistry_Expert interprets/model-checks results, and Verification_Expert audits and finalizes the output (recommended ball).",
+    "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": "Statistical Simulation & Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Theoretical Modeling & Interpretation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Result Verification & Reporting",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Game Riddle: 'Pick That Ping-Pong' Question",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Recommended Ball Number (simulation result)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Contestant / End User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_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_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "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": "Probability_Expert made an error in the simulation implementation, producing an incorrect outcome (simulation output disagrees with ground truth).",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "AGENT_ERROR",
+        "description": "Verification_Expert and collaborators accepted the simulation result without detecting the implementation error, allowing incorrect conclusion to be finalized.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_003"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require an analytical/deterministic derivation (Markov-chain or combinatorial analysis) alongside Monte Carlo simulation to cross-check and validate simulation outcomes before finalizing recommendations.",
+        "affected_ids": [
+          "agent_001",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Introduce automated unit tests, deterministic seed logging, and result-audit hooks in the Computer_terminal execution environment so that simulation correctness is verifiable and reproducible; require Verification_Expert to run audits before acceptance.",
+        "affected_ids": [
+          "tool_001",
+          "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..."
+}