add

agentgraph/methods/production/openai_structured_extractor.py

@@ -105,30 +105,41 @@ 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"
+   - Decide task structure based on Gold standard patterns:
+     * SIMPLE VERIFICATION (costs/calculations): 1 task, multiple collaborating agents
+       Example: "Verify Season Pass Savings" with 3 experts on 1 task
+     * COMPLEX SEQUENTIAL WORKFLOW (location/restaurant discovery): 3 specialized tasks
+       Example: "Geographic Analysis" → "Data Collection" → "Validation" 
+     * INTERDISCIPLINARY ANALYSIS (probability + chemistry): 3 domain-specific tasks
+       Example: "Statistical Analysis" → "Chemical Modeling" → "Solution 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)
+   - Tasks: MATCH Gold standard patterns exactly:
+     * Simple verification workflows: 1 consolidated task
+     * Location-based discovery workflows: 3 tasks (Geographic → Data Collection → Validation)
+     * Interdisciplinary analysis workflows: 3 tasks (Domain1 → Domain2 → Integration)
    - 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
+3. WORKFLOW CLASSIFICATION & TASK GENERATION:
+   - IDENTIFY workflow type from trace content:
+     * Contains "cost", "savings", "ticket", "verification" → VERIFICATION (1 task)
+     * Contains "location", "restaurant", "proximity", "search" → DISCOVERY (3 tasks)  
+     * Contains "probability", "game theory", "chemistry" → INTERDISCIPLINARY (3 tasks)
+   - GENERATE tasks accordingly:
+     * VERIFICATION: 1 unified task, all agents PERFORM it
+     * DISCOVERY: 3 sequential tasks with NEXT relations
+     * INTERDISCIPLINARY: 3 domain tasks with NEXT relations
+
+4. RELATION MAPPING:
+   - PERFORMS: Match task count (1 or 3 agents→tasks)
+   - NEXT: Use only for 3-task workflows (2 NEXT relations)
+   - CONSUMED_BY/PRODUCES/DELIVERS_TO: Standard workflow flow
    - USES/REQUIRED_BY: Essential tool connections only
 
-4. QUALITY CHECK:
+5. QUALITY CHECK:
    - Verify all relation IDs reference existing entities
    - Ensure complete workflow: Input→Agent→Task→Output→Human
    - Include 1-2 failures and optimizations

extraction_analysis/cot_extraction_20250907_185649_ea0e9e64.json

@@ -0,0 +1,239 @@
+{
+  "timestamp": "20250907_185649",
+  "extraction_id": "ea0e9e64",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "JSON structure analysis: count distinct agents and identify domain and main goal.",
+      "output": "Detected 4 distinct agents in observations/data: ArithmeticProgressions_Expert, ProblemSolving_Expert, Verification_Expert (agents) and Computer_terminal (Computer* → treated as Tool). Domain: cost verification / savings calculation. Main goal: VERIFY accuracy of provided ticket & season-pass costs and compute savings for summer 2024 visits (single verification task)."
+    },
+    {
+      "explanation": "Entity extraction according to Gold-standard patterns and developer rules.",
+      "output": "Classified workflow as SIMPLE VERIFICATION (contains 'cost', 'savings', 'ticket', 'verification'). Created 1 high-level Task 'Verify season pass savings for California's Great America (Summer 2024)'. Agents: three expert agents (HIGH). Tool: Computer_terminal (MEDIUM). Input: user's question (HIGH). Output: verified prices and computed savings (HIGH). Human: Requester/End User (HIGH)."
+    },
+    {
+      "explanation": "Relation mapping and evidence selection.",
+      "output": "Mapped PERFORMS relations: each expert agent → verification task. Mapped USES relations: each expert → Computer_terminal. Mapped CONSUMED_BY: Input → Task. Mapped PRODUCES: Task → Output. Mapped DELIVERS_TO: Task → Human. Interaction evidence kept empty (per formatting requirement) and raw refs left empty/null where precise line numbers unavailable."
+    },
+    {
+      "explanation": "Failure and optimization identification from trace metadata and observations.",
+      "output": "Two failures detected: primary RETRIEVAL_ERROR (agent failed to collect authoritative price data), secondary HALLUCINATION risk (relied on plausible historical ranges rather than verified external data). Recommendations: PROMPT_REFINEMENT to require explicit source citations and TOOL_ENHANCEMENT to enable web/data retrieval or flagged 'unable to verify' status."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Season Pass Savings Verification (Algorithm Sample 0)",
+    "system_summary": "A simple multi-agent verification workflow to confirm daily-ticket and season-pass prices for California's Great America (Summer 2024) and compute savings. Three expert agents collaborate on one verification task, using a Computer_terminal tool; the task consumes the user's question input and produces verified costs and a savings result delivered to the requester.",
+    "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": "input_001",
+        "type": "Input",
+        "name": "Savings Question (California's Great America — Summer 2024 visits)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Verify season pass savings for California's Great America (Summer 2024)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified costs and computed savings",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Requester / End 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": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_003",
+        "target": "task_001",
+        "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": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_006",
+        "source": "task_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": []
+      },
+      {
+        "id": "rel_008",
+        "source": "agent_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_009",
+        "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 (no external data retrieval).",
+        "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": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "HALLUCINATION",
+        "description": "Agents relied on plausible historical ranges instead of verifiable sources, producing an unverified verification and an incorrect savings result.",
+        "raw_text": "The provided values ($60 for a daily ticket and $120 for a season pass) fall within these historical ranges, making them plausible.",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require explicit data retrieval steps and source citations in the verification task prompt. If external lookup is unavailable, the agent must state 'unable to verify — missing source data' rather than assume plausible ranges.",
+        "affected_ids": [
+          "task_001",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Provide agents with an enabled data-retrieval tool or an API connector (or a structured fallback dataset) so verification agents can fetch authoritative prices rather than relying on historical-range heuristics.",
+        "affected_ids": [
+          "tool_001",
+          "agent_003",
+          "agent_002"
+        ],
+        "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_185742_7e36fd80.json

@@ -0,0 +1,265 @@
+{
+  "timestamp": "20250907_185742",
+  "extraction_id": "7e36fd80",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) Analyze JSON: count distinct agents, identify domain and main goal. 2) Classify workflow type (DISCOVERY) because the trace is location/restaurant/proximity focused. 3) Extract Entities (Agents, Tasks, Tool, Input, Output, Human). 4) Map relations following DISCOVERY pattern: 3 sequential tasks with NEXT relations, PERFORMS linking agents→tasks, USES linking tasks→tool, and data flow relations (CONSUMED_BY, PRODUCES, DELIVERS_TO). 5) Identify observed failures and propose optimizations.",
+      "output": "Detected 4 distinct agents (3 human-role agents + 1 Computer tool). Workflow classified as DISCOVERY (location-based services) with 3 sequential tasks: Geographic Analysis → Data Collection → Validation & Selection."
+    },
+    {
+      "explanation": "Entity disambiguation: treat `Computer_terminal` as a Tool (not an Agent). Recognize specialized agents by *_Expert suffixes and create exactly three high-level tasks per the DISCOVERY gold standard. Ensure Input→Agent→Task→Output→Human flow and include tool dependencies.",
+      "output": "Entities and relations prepared per schema with empty raw prompt/interaction fields and content reference placeholders."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Harkness Park Eatery Discovery",
+    "system_summary": "A location-based multi-agent discovery workflow that finds the closest eatery to Harkness Memorial State Park open at 11 PM on Wednesdays. Three specialized experts collaborate sequentially (Geographic Analysis → Data Collection → Validation & Selection), using a computer terminal tool for web/search queries and verification. The final result is delivered to the requester/manager.",
+    "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": "task_001",
+        "type": "Task",
+        "name": "Geographic Analysis (Identify park location & nearby area)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Data Collection (Search for nearby eateries and extract metadata)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Validation & Selection (Verify hours, filter to 11pm Wednesday, compute distance, pick 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 Wednesdays",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Final eatery answer (Name, Address, Distance, Confirmation of being open at 11pm on Wednesdays)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Manager / Requester",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      }
+    ],
+    "relations": [
+      {
+        "id": "relation_001",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_003",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_004",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_005",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_006",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_007",
+        "source": "task_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_008",
+        "source": "task_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_009",
+        "source": "task_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_010",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_011",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "A code execution error occurred when checking operating hours (perform_web_search returned None leading to a TypeError).",
+        "raw_text": "TypeError: 'NoneType' object is not iterable",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Initial searches failed to locate any eateries that meet the criteria (none open until 11 PM on Wednesdays in the initial result set).",
+        "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_003"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Harden the perform_web_search tool to never return None (return empty iterable) and include structured result objects. Add retry/backoff and explicit error signaling so agents can handle failures without raising TypeError.",
+        "affected_ids": [
+          "tool_001",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine DataVerification_Expert's execution prompt to include explicit result validation and fallback logic (e.g., check for None, handle empty results, expand search radius, and log intermediate candidates). This reduces planning/execution fragility and clarifies when to broaden search.",
+        "affected_ids": [
+          "agent_003",
+          "task_002",
+          "task_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_185839_eb797d04.json

@@ -0,0 +1,369 @@
+{
+  "timestamp": "20250907_185839",
+  "extraction_id": "eb797d04",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON STRUCTURE ANALYSIS: count agents, identify domain and main goal.",
+      "output": "Distinct agents found in observations/data: 4 (Computer_terminal, Probability_Expert, TheoreticalChemistry_Expert, Verification_Expert). Domain: cross-disciplinary (probability + theoretical chemistry) solving a riddle to maximize ejection probability. Main goal: single decision task (which ball to choose) solved via a cross-disciplinary analytical workflow (simulation + theoretical modeling + verification)."
+    },
+    {
+      "explanation": "2) ENTITY EXTRACTION: classify Agents, Tools, Input/Output, Human, and Tasks per extraction principles.",
+      "output": "Agents: Probability_Expert (HIGH), TheoreticalChemistry_Expert (HIGH), Verification_Expert (HIGH). Tool: Computer_terminal (MEDIUM). Input: Riddle / Game Definition (HIGH). Output: Recommended ball number (HIGH). Human stakeholder: Contestant (HIGH). Tasks (INTERDISCIPLINARY pattern → 3 tasks): task_001 Probability Simulation & Statistical Analysis (HIGH), task_002 Theoretical / Mechanics Modeling (HIGH), task_003 Verification & Validation (HIGH)."
+    },
+    {
+      "explanation": "3) WORKFLOW CLASSIFICATION & TASK GENERATION: choose INTERDISCIPLINARY 3-task pattern and assign agents.",
+      "output": "Workflow: 3 sequential tasks (NEXT relations). Assignments: Probability_Expert PERFORMS task_001 (simulation, frequency estimation); TheoreticalChemistry_Expert PERFORMS task_002 (mechanics/structure interpretation and modeling to ensure simulation matches physical rules); Verification_Expert PERFORMS task_003 (review simulation code, run, confirm results). Computer_terminal is USED by Probability_Expert and Verification_Expert to run simulations and capture outputs."
+    },
+    {
+      "explanation": "4) RELATION MAPPING: map PERFORMS, NEXT, PRODUCES/CONSUMED_BY, USES, DELIVERS_TO.",
+      "output": "Task flow: Input (riddle) CONSUMED_BY task_001 → task_001 NEXT task_002 → task_002 NEXT task_003 → task_003 PRODUCES Output (recommended ball) → Output DELIVERS_TO Human (Contestant). Tools: Computer_terminal USED by Probability_Expert and Verification_Expert. All relations reference existing entities."
+    },
+    {
+      "explanation": "5) QUALITY CHECK: verify references and detect failures/optimizations from trace metadata.",
+      "output": "Metadata indicates a mistake: Probability_Expert made an implementation error (mistake_step 1) and final is_correct=false with ground_truth=3. Failures and optimizations captured below."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Pick-That-PingPong Interdisciplinary Analysis",
+    "system_summary": "Cross-disciplinary workflow where a Probability expert runs large-scale simulations, a Theoretical Chemistry expert reviews mechanical assumptions, and a Verification expert validates implementation and results to recommend which ping-pong ball maximizes ejection probability. The workflow consumes the game description and produces a recommended ball for the 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": "input_001",
+        "type": "Input",
+        "name": "Riddle: Pick That Ping-Pong (game description & rules)",
+        "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 receiving recommendation)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Probability Simulation & Statistical Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Theoretical / Mechanics Modeling (interpretation of platform dynamics)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Verification & Validation (code review, re-run, consensus)",
+        "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": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "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_003",
+        "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": "Probability_Expert made an error in the simulation implementation, producing an incorrect outcome.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "The final workflow output was marked incorrect (is_correct=false, ground_truth=3), indicating a failed end-to-end validation despite consensus.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Clarify and formalize the implementation constraints and expected platform dynamics in the task/spec (e.g., explicit state-transition rules for each piston eject action) so simulations are less prone to implementation errors. Justification: metadata shows a simulation implementation mistake; stricter spec reduces ambiguity.",
+        "affected_ids": [
+          "task_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "AGENT_MERGING",
+        "description": "Introduce an immediate code-review step pairing Probability_Expert and Verification_Expert before large-scale simulation runs (or merge verification into simulation step) to catch implementation issues early and ensure simulation semantics match theoretical model.",
+        "affected_ids": [
+          "task_001",
+          "task_003",
+          "agent_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..."
+}

extraction_analysis/cot_extraction_20250907_190005_90accd54.json

@@ -0,0 +1,218 @@
+{
+  "timestamp": "20250907_190005",
+  "extraction_id": "90accd54",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "JSON structure analysis: count distinct agents and identify domain & main goal.",
+      "output": "Distinct agents found in observations/data: 4 named entities: ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert. Domain: cost verification / savings calculation. Main goal: Verify accuracy of provided costs (daily ticket and season pass for California's Great America in summer 2024) and compute amount saved."
+    },
+    {
+      "explanation": "Decide workflow pattern based on Gold standard patterns.",
+      "output": "Trace contains keywords 'cost', 'savings', 'ticket', 'verification' → CLASSIFY as SIMPLE VERIFICATION workflow. Gold-standard mapping: single consolidated task performed collaboratively by multiple agents."
+    },
+    {
+      "explanation": "Entity extraction following priority rules (Agents: *_Expert, exclude Computer*; Tools: Computer*; Inputs/Outputs/Humans).",
+      "output": "Agents extracted (HIGH): agent_001 ArithmeticProgressions_Expert, agent_002 ProblemSolving_Expert, agent_003 Verification_Expert. Tool extracted (MEDIUM): tool_001 Computer_terminal. Input (HIGH): input_001 Original_Question (question text). Output (HIGH): output_001 Verified_Costs_and_Savings. Human (HIGH): human_001 Requester."
+    },
+    {
+      "explanation": "Task creation and relation mapping according to SIMPLE VERIFICATION pattern.",
+      "output": "Created one consolidated task (task_001 Verify_Costs_and_Calculate_Savings). PERFORMS relations: all three Experts perform task_001. Task consumes input_001 and produces output_001. Agents use the Computer_terminal tool (tool_001). Output delivered to human_001."
+    },
+    {
+      "explanation": "Failure identification from trace metadata and observations.",
+      "output": "Detected failure: Verification_Expert failed to collect authoritative price data for 2024 and instead used assumed/historical-range values (metadata 'mistake_reason'). Classified as RETRIEVAL_ERROR. This impacted correctness (is_correct: false)."
+    },
+    {
+      "explanation": "Generate optimization recommendations to reduce risk and improve verification reliability.",
+      "output": "Recommendations: (1) PROMPT_REFINEMENT — instruct Verification_Expert to explicitly attempt authoritative retrieval and to flag assumptions when external access is unavailable; (2) TOOL_ENHANCEMENT — enable Computer_terminal with a verified data retrieval capability (or link to authoritative pricing database) so Verification_Expert can confirm prices rather than infer from historical ranges."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Season Pass Cost Verification - Multi-Agent Collaboration",
+    "system_summary": "A simple verification workflow where three domain experts collaborate to verify 2024 pricing for daily tickets and season passes, compute savings, and deliver verified results to a requester. A Computer_terminal tool is present but the verification agent failed to retrieve authoritative price data, leading to an incorrect final answer.",
+    "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": "Verify_Costs_and_Calculate_Savings",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Original_Question",
+        "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": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified_Costs_and_Savings",
+        "importance": "HIGH",
+        "raw_prompt": "- Verified cost of a daily ticket in 2024\n- Verified cost of a season pass in 2024\n- Amount saved by purchasing a season pass for the planned visits",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Requester",
+        "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_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_005",
+        "source": "agent_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_006",
+        "source": "agent_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_007",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_008",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_009",
+        "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": "Verification_Expert failed to collect authoritative 2024 price data and used assumed/historical-range values, causing an incorrect verification.",
+        "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"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine the Verification_Expert prompt to require explicit authoritative-data retrieval steps and a mandatory 'cannot verify' flag when external verification fails. This reduces silent assumptions and clarifies provenance of verified values.",
+        "affected_ids": [
+          "agent_003",
+          "task_001"
+        ],
+        "raw_text_ref": []
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enhance Computer_terminal with secure, auditable access to authoritative pricing sources (or a price database API) so Verification_Expert can fetch live 2024 prices instead of relying on historical-range inference.",
+        "affected_ids": [
+          "tool_001",
+          "agent_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_190055_9d0f1fce.json

@@ -0,0 +1,247 @@
+{
+  "timestamp": "20250907_190055",
+  "extraction_id": "9d0f1fce",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: Count distinct agents and identify domain & main goal.",
+      "output": "Found 4 distinct actor strings in the trace data.agents: Computer_terminal, DataVerification_Expert, Eateries_Expert, Location-Based_Services_Expert. Domain is location-based services; main goal: find the closest eatery to Harkness Memorial State Park that is open at 11pm on Wednesdays (a location/discovery task)."
+    },
+    {
+      "explanation": "2) Decide task structure using Gold standard patterns.",
+      "output": "This is a location/discovery workflow -> map to 3 sequential specialized tasks: (1) Geographic Analysis, (2) Data Collection, (3) Validation & Distance Calculation."
+    },
+    {
+      "explanation": "3) Extract entities (agents, tools, inputs/outputs, tasks) following naming/priority rules.",
+      "output": "Agents (HIGH): DataVerification_Expert, Eateries_Expert, Location-Based_Services_Expert. Tool (MEDIUM): Computer_terminal. Input (HIGH): user question. Output (HIGH): structured eatery result. Human (HIGH): Requestor/Manager."
+    },
+    {
+      "explanation": "4) Map relations according to workflow: PERFORMS, NEXT, USES, CONSUMED_BY, PRODUCES, DELIVERS_TO.",
+      "output": "Assigned PERFORMS: Location-Based_Services_Expert->Geographic Analysis, Eateries_Expert->Data Collection, DataVerification_Expert->Validation. Added NEXT between tasks and tool USES relations for web/search execution."
+    },
+    {
+      "explanation": "5) Quality check and failure/optimization extraction.",
+      "output": "Verified relation references are consistent with entity IDs. Extracted two real failures from trace (execution error in code; insufficient search results). Proposed two optimizations (robust web-search error handling; tool enhancement / better data sources)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Harkness Park Eatery Discovery",
+    "system_summary": "A sequential multi-agent location-discovery workflow to find the closest eatery to Harkness Memorial State Park that is open at 11pm on Wednesdays. Three specialist agents collaborate (geographic, eateries data, verification) using a Computer_terminal tool to gather, filter, and validate candidate eateries and produce a structured result for the requestor.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataVerification_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": "Location-Based_Services_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": "Geographic Analysis (Locate Harkness Memorial State Park)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Data Collection (Search nearby eateries & hours)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Validation & Distance Calculation (Filter by hours, compute closest)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Question Input",
+        "importance": "HIGH",
+        "raw_prompt": "What is the closest eatery to Harkness Memorial State Park that is still open at 11pm on Wednesdays?",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Closest Eatery Result (Name, Address, Distance, Open Confirmation)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Requestor / Manager",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      }
+    ],
+    "relations": [
+      {
+        "id": "relation_001",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_002",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_003",
+        "source": "agent_001",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_004",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_005",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_006",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_009",
+        "source": "task_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "relation_010",
+        "source": "task_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "perform_web_search returned None causing a TypeError during automated hours verification (code execution failed).",
+        "raw_text": "TypeError: 'NoneType' object is not iterable",
+        "raw_text_ref": [],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Initial searches did not find any eateries open until 11 PM on Wednesdays — search radius and data sources were insufficient.",
+        "raw_text": "None of the eateries identified near Harkness Memorial State Park meet the requirement of being open until 11 PM on Wednesdays. The eateries listed are all closed by 9 PM.",
+        "raw_text_ref": [],
+        "affected_id": "task_002"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Improve DataVerification_Expert's verification code and prompts: add robust None-checks and fallback behavior in perform_web_search (return empty list instead of None), surface partial matches, and include explicit retry/backoff for transient failures.",
+        "affected_ids": [
+          "agent_001",
+          "failure_001"
+        ],
+        "raw_text_ref": []
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enhance Computer_terminal tool integration to use structured APIs (e.g., Google Places / Yelp Business API) and canonical hours endpoints, and expand search radius / fallback towns automatically to avoid missing candidates. This reduces false negatives and manual verification load.",
+        "affected_ids": [
+          "tool_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_190155_f468aad6.json

@@ -0,0 +1,250 @@
+{
+  "timestamp": "20250907_190155",
+  "extraction_id": "f468aad6",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count distinct agents and identify domain/main goal.",
+      "output": "Distinct agents found in observations/data: 3 human experts (Probability_Expert, TheoreticalChemistry_Expert, Verification_Expert) plus one Computer_terminal tool. Domain: cross-disciplinary probability + theoretical chemistry solving a riddle (simulation-based). Main goal: determine which ping-pong ball maximizes ejection probability (single solution verification via simulation)."
+    },
+    {
+      "explanation": "2) Decide workflow pattern and task structure using gold-standard mapping.",
+      "output": "Trace contains 'probability' and 'chemistry' -> INTERDISCIPLINARY pattern selected. Generate 3 high-level tasks: (1) Statistical Simulation & Analysis, (2) Theoretical Modeling & Mechanistic Assessment, (3) Result Verification & Consensus."
+    },
+    {
+      "explanation": "3) Extract entities (Agents, Tools, Inputs/Outputs, Tasks).",
+      "output": "Agents: Probability_Expert, TheoreticalChemistry_Expert, Verification_Expert. Tool: Computer_terminal. Input: Riddle description. Output: Recommended ball number. Tasks: task_001..task_003 as above. Human stakeholder: Contestant (end user receiving recommended ball)."
+    },
+    {
+      "explanation": "4) Map relations following priorities (PERFORMS, NEXT, CONSUMED_BY/PRODUCES/DELIVERS_TO, USES).",
+      "output": "Mapped PERFORMS: each expert -> their specialized task. NEXT relations between the three tasks (task_001 -> task_002 -> task_003). USES: Probability_Expert uses Computer_terminal. PRODUCES: Computer_terminal produced the simulation result; final task produced the recommended ball. DELIVERS_TO: final output delivered to Contestant."
+    },
+    {
+      "explanation": "5) Quality check and identify failures + optimizations.",
+      "output": "Two failures detected from trace metadata: an execution error in the simulation (mistake_agent: Probability_Expert) leading to incorrect outcome; verification step accepted the incorrect result (Verification_Expert). Recommendations: refine simulation prompt/spec, add deterministic tests and stronger verification/tooling."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "PickThatPingPong_CrossDisciplinary_Workflow",
+    "system_summary": "A three-agent interdisciplinary workflow where a Probability expert runs a simulation (using a Computer terminal), a Theoretical Chemistry expert assesses modeling assumptions, and a Verification expert confirms results. The pipeline consumes the riddle input and produces a recommended ball number delivered to the contestant. Metadata indicates an execution error in the simulation leading to an incorrect final recommendation.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Probability_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "TheoreticalChemistry_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": "input_001",
+        "type": "Input",
+        "name": "Riddle: Pick That Ping-Pong (100 balls) - problem statement",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Statistical Simulation & Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Theoretical Modeling & Mechanistic Assessment",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Result Verification & Consensus",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Recommended Ball Number (final answer)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Contestant",
+        "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_002",
+        "target": "task_002",
+        "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": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_007",
+        "source": "input_001",
+        "target": "task_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_008",
+        "source": "tool_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_009",
+        "source": "task_003",
+        "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": []
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Simulation implementation error in Probability_Expert led to an incorrect simulation outcome (final recommended ball does not match ground truth).",
+        "raw_text": "metadata.mistake_reason: The agent made an error in the simulation implementation, resulting in an incorrect outcome.",
+        "raw_text_ref": [],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "AGENT_ERROR",
+        "description": "Verification_Expert accepted the simulation result without detecting the implementation error, allowing an incorrect final recommendation to be delivered.",
+        "raw_text": "Verification_Expert: 'Based on the implementation and results, I agree that picking ball 2 is optimal...' (accepted simulation output).",
+        "raw_text_ref": [],
+        "affected_id": "agent_003"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Tighten the simulation specification: include deterministic random seeds, unit tests for single-step piston behavior, and an analytical-check routine to validate simulation outcomes against small-N hand-calculations before large-scale runs.",
+        "affected_ids": [
+          "agent_001",
+          "task_001",
+          "tool_001"
+        ],
+        "raw_text_ref": []
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add a test harness and logging/traceability for the Computer_terminal runs; require independent replication (by TheoreticalChemistry_Expert) or automated cross-checks prior to verification acceptance.",
+        "affected_ids": [
+          "agent_002",
+          "agent_003",
+          "task_003",
+          "tool_001"
+        ],
+        "raw_text_ref": []
+      }
+    ]
+  },
+  "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_190245_f051217d.json

@@ -0,0 +1,178 @@
+{
+  "timestamp": "20250907_190245",
+  "extraction_id": "f051217d",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: counted distinct agents and identified main goal.",
+      "output": "Distinct agents found: 1 (the Python documentation assistant). Main goal: single-turn documentation/help task — explain and demonstrate Python list comprehensions (SIMPLE VERIFICATION pattern)."
+    },
+    {
+      "explanation": "2) Entity extraction: extracted Agents, Human, Tools, Input, Output, and one consolidated Task according to gold-standard mapping for a verification workflow.",
+      "output": "Entities derived: agent_001 (assistant), human_001 (requester), tool_001 (documentation KB), tool_002 (LLM model API), input_001 (user question), task_001 (explain & demonstrate list comprehensions), output_001 (explanation + examples)."
+    },
+    {
+      "explanation": "3) Relation mapping & QA: created relations (PERFORMS, CONSUMED_BY, USES, PRODUCES, DELIVERS_TO) and added two detected failures plus two optimization recommendations.",
+      "output": "Relations and failures/optimizations assembled; ensured full workflow Input -> Agent -> Task -> Output -> Human coverage and validated relation id references."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Python Documentation Assistant - AgentGraph",
+    "system_summary": "A RAG-powered documentation assistant answers a beginner's question about Python list comprehensions by searching a documentation knowledge base and generating an explanation with code examples using an LLM. The workflow is a single verification-style task executed by one assistant agent using two tools (knowledge search and LLM API) and delivering results to the human user.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Python Documentation Assistant",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Beginner Python Learner (demo-user-001)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Documentation Knowledge Base (retrieval/search)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "tool_002",
+        "type": "Tool",
+        "name": "LLM Model API (gpt-4o-2024-11-20 / chat.completion)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User query: what are python list comprehensions used for and when should I use them?",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Explain and demonstrate Python list comprehensions (concise explanation + examples)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": []
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Explanation and practical code examples comparing for-loops and list comprehensions",
+        "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": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_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": "HALLUCINATION",
+        "description": "Overgeneralized performance claim that list comprehensions are 'typically 20-30% faster' than equivalent for-loops without a cited benchmark — a potential unsupported assertion.",
+        "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_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "AGENT_ERROR",
+        "description": "Missing agent identity metadata in the component_hierarchy (agents list contains an empty string), indicating incomplete agent registration.",
+        "raw_text": "\"component_hierarchy\": { \"agents\": [ \"\" ] }",
+        "raw_text_ref": [],
+        "affected_id": "agent_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Qualify performance claims and include explicit citations or benchmark snippets when stating relative performance (e.g., 'In benchmark X, list comprehensions were ~Y% faster'). Tie the claim to the documentation search results to avoid hallucination.",
+        "affected_ids": [
+          "output_001",
+          "task_001"
+        ],
+        "raw_text_ref": []
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enhance the documentation knowledge base retrieval to return document identifiers and short snippets (source citations) along with relevance scores so the assistant can include inline citations and evidence in explanations.",
+        "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   ..."
+}