add

agentgraph/methods/production/openai_structured_extractor.py

@@ -95,7 +95,47 @@ OUTPUT REQUIREMENTS:
 - Complete workflow: Input→Agent→Task→Output→Human
 - ID format: agent_001, task_001, etc.
 - Empty raw_prompt/interaction_prompt fields
-- Include 1-2 failures and optimizations"""
+- Include 1-2 failures and optimizations
+
+== FEW-SHOT EXAMPLE (COMPLEX DISCOVERY WORKFLOW) ==
+
+Input Query: "What is the closest eatery to Harkness Memorial State Park open at 11pm on Wednesdays?"
+
+Expected Output Structure:
+{
+  "system_name": "Location-Based Restaurant Discovery System",
+  "system_summary": "Multi-agent system for location-based restaurant discovery with time constraints...",
+  "entities": [
+    {"id": "agent_001", "type": "Agent", "name": "Location-Based Services Expert", "importance": "HIGH", "raw_prompt": "", "raw_prompt_ref": [{"line_start": 15, "line_end": 25}]},
+    {"id": "agent_002", "type": "Agent", "name": "Eateries Expert", "importance": "HIGH", "raw_prompt": "", "raw_prompt_ref": [{"line_start": 35, "line_end": 45}]},
+    {"id": "agent_003", "type": "Agent", "name": "Data Verification Expert", "importance": "HIGH", "raw_prompt": "", "raw_prompt_ref": [{"line_start": 55, "line_end": 65}]},
+    {"id": "agent_004", "type": "Tool", "name": "Computer Terminal", "importance": "MEDIUM", "raw_prompt": "", "raw_prompt_ref": [{"line_start": 75, "line_end": 80}]},
+    {"id": "task_001", "type": "Task", "name": "Geographic Proximity Analysis", "importance": "HIGH", "raw_prompt": "", "raw_prompt_ref": [{"line_start": 5, "line_end": 10}]},
+    {"id": "task_002", "type": "Task", "name": "Restaurant Data Collection", "importance": "HIGH", "raw_prompt": "", "raw_prompt_ref": [{"line_start": 25, "line_end": 35}]},
+    {"id": "task_003", "type": "Task", "name": "Operating Hours Validation", "importance": "HIGH", "raw_prompt": "", "raw_prompt_ref": [{"line_start": 45, "line_end": 55}]},
+    {"id": "input_001", "type": "Input", "name": "User Restaurant Query", "importance": "HIGH", "raw_prompt": "", "raw_prompt_ref": [{"line_start": 1, "line_end": 3}]},
+    {"id": "output_001", "type": "Output", "name": "Restaurant Recommendations", "importance": "HIGH", "raw_prompt": "", "raw_prompt_ref": [{"line_start": 90, "line_end": 95}]},
+    {"id": "human_001", "type": "Human", "name": "End User", "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": 5, "line_end": 8}]},
+    {"id": "rel_002", "source": "agent_001", "target": "task_001", "type": "PERFORMS", "importance": "HIGH", "interaction_prompt": "", "interaction_prompt_ref": [{"line_start": 15, "line_end": 25}]},
+    {"id": "rel_003", "source": "agent_002", "target": "task_002", "type": "PERFORMS", "importance": "HIGH", "interaction_prompt": "", "interaction_prompt_ref": [{"line_start": 35, "line_end": 45}]},
+    {"id": "rel_004", "source": "agent_003", "target": "task_003", "type": "PERFORMS", "importance": "HIGH", "interaction_prompt": "", "interaction_prompt_ref": [{"line_start": 55, "line_end": 65}]},
+    {"id": "rel_005", "source": "task_001", "target": "task_002", "type": "NEXT", "importance": "HIGH", "interaction_prompt": "", "interaction_prompt_ref": [{"line_start": 25, "line_end": 30}]},
+    {"id": "rel_006", "source": "task_002", "target": "task_003", "type": "NEXT", "importance": "HIGH", "interaction_prompt": "", "interaction_prompt_ref": [{"line_start": 45, "line_end": 50}]},
+    {"id": "rel_007", "source": "task_003", "target": "output_001", "type": "PRODUCES", "importance": "HIGH", "interaction_prompt": "", "interaction_prompt_ref": [{"line_start": 90, "line_end": 95}]},
+    {"id": "rel_008", "source": "output_001", "target": "human_001", "type": "DELIVERS_TO", "importance": "HIGH", "interaction_prompt": "", "interaction_prompt_ref": [{"line_start": 95, "line_end": 100}]}
+  ],
+  "failures": [
+    {"id": "failure_001", "description": "Data Verification Expert failed to validate restaurant hours due to incorrect implementation", "affected_id": "agent_003", "risk_type": "EXECUTION_ERROR"}
+  ],
+  "optimizations": [
+    {"id": "opt_001", "description": "Enhance location services with caching mechanisms", "affected_ids": ["agent_001"], "recommendation_type": "TOOL_ENHANCEMENT"}
+  ]
+}
+
+This example shows: 3 agents, 3 sequential tasks, complete NEXT chain, proper tool classification."""
 
         # User prompt - Streamlined and focused
         user_prompt = f"""Extract a knowledge graph from this trace using systematic reasoning steps.

extraction_analysis/cot_extraction_20250907_191232_ec87da6a.json

@@ -0,0 +1,378 @@
+{
+  "timestamp": "20250907_191232",
+  "extraction_id": "ec87da6a",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: counted distinct agents in data/observations (excluding Computer_terminal which is a tool). Identified domain as location-based services with a single multi-step goal (find closest eatery open at 11pm Wednesday). Execution logs show multiple search attempts and an execution error in DataVerification_Expert.",
+      "output": "Distinct agents: 3 specialists (Location-Based_Services_Expert, Eateries_Expert, DataVerification_Expert). Tool: Computer_terminal. Workflow type: COMPLEX SEQUENTIAL WORKFLOW (location/discovery)."
+    },
+    {
+      "explanation": "2) Entity extraction: extracted three high-priority agents (matching *_Expert pattern), the Computer_terminal tool, one input, one output, and 3 sequential tasks following the gold standard (Geographic → Data Collection → Validation).",
+      "output": "Entities drafted: 3 Agent nodes, 1 Tool node, 3 Task nodes, 1 Input, 1 Output, 1 Human consumer."
+    },
+    {
+      "explanation": "3) Relation mapping: assigned PERFORMS relations from each specialist to its domain task, created NEXT relations to form a 3-step chain, connected Input→first agent via CONSUMED_BY, task→output via PRODUCES and output→human via DELIVERS_TO, and added USES relations linking agents to the Computer_terminal tool.",
+      "output": "Relations: PERFORMS (3), NEXT (2), CONSUMED_BY (1), PRODUCES (1), DELIVERS_TO (1), USES (3)."
+    },
+    {
+      "explanation": "4) Quality checks, failures, and optimizations: verified all relations reference existing entity IDs. Located two failure events in the trace (incorrect Python code by DataVerification_Expert causing execution failure; perform_web_search returned None causing a TypeError). Proposed two optimizations: strengthen DataVerification error handling and add caching/fallback data sources for location searches.",
+      "output": "Failures and optimizations recorded."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Location-Based Restaurant Discovery System",
+    "system_summary": "Multi-agent location-based discovery pipeline that finds the closest eatery meeting time-based constraints. Three specialists (Location services, Eateries, Data verification) collaborate sequentially using a Computer terminal tool to search, aggregate, and validate operating hours before producing a recommendation to the end user.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Location-Based Services Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Eateries Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Data Verification Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 60,
+            "line_end": 90
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer Terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 50
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Geographic Proximity Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 13,
+            "line_end": 20
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Restaurant Data Collection",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 21,
+            "line_end": 36
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Operating Hours Validation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 37,
+            "line_end": 58
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Restaurant Query",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Closest Eatery Recommendation (name, address, distance, open confirmation)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 90,
+            "line_end": 96
+          }
+        ]
+      },
+      {
+        "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": 13,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 21,
+            "line_end": 30
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 10
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 60,
+            "line_end": 90
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 21,
+            "line_end": 36
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 37,
+            "line_end": 58
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 90,
+            "line_end": 96
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 90,
+            "line_end": 96
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 36
+          }
+        ]
+      },
+      {
+        "id": "rel_010",
+        "source": "agent_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 20
+          }
+        ]
+      },
+      {
+        "id": "rel_011",
+        "source": "agent_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 60,
+            "line_end": 90
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "DataVerification_Expert executed Python code that raised a TypeError due to a None result from perform_web_search, preventing operating-hours validation.",
+        "raw_text": "TypeError: 'NoneType' object is not iterable",
+        "raw_text_ref": [
+          {
+            "line_start": 70,
+            "line_end": 74
+          }
+        ],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Web-search function returned None for some queries (perform_web_search returned None), causing incomplete data retrieval.",
+        "raw_text": "The error indicates that the `perform_web_search` function returned `None`",
+        "raw_text_ref": [
+          {
+            "line_start": 64,
+            "line_end": 68
+          }
+        ],
+        "affected_id": "tool_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Strengthen Data Verification agent prompts and code routines to handle None/empty search results and add defensive checks around perform_web_search; include explicit fallback logic and retries in the agent's instruction set.",
+        "affected_ids": [
+          "agent_003",
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 60,
+            "line_end": 90
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add caching and multiple data-source fallbacks (e.g., Yelp, Tripadvisor, OpenTable APIs) in the Computer Terminal tool integration to improve retrieval robustness and reduce dependence on single-search responses.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 20,
+            "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_191341_2a6f8473.json

@@ -0,0 +1,367 @@
+{
+  "timestamp": "20250907_191341",
+  "extraction_id": "2a6f8473",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count distinct agents and identify domain & main goal.",
+      "output": "Distinct agent identifiers found in data.agents: 4 (Computer_terminal, Probability_Expert, TheoreticalChemistry_Expert, Verification_Expert). Domain: cross-disciplinary probability + theoretical chemistry. Main goal: single riddle-solving workflow to determine which ping-pong ball maximizes ejection probability (INTERDISCIPLINARY_ANALYSIS pattern)."
+    },
+    {
+      "explanation": "2) Decide task structure using Gold-standard patterns for interdisciplinary workflows.",
+      "output": "Selected 3 high-level sequential tasks: (1) Statistical Simulation & Analysis, (2) Theoretical/Mechanistic Modeling, (3) Solution Verification & Recommendation."
+    },
+    {
+      "explanation": "3) Extract entities (Agents, Tools, Inputs/Outputs, Tasks) and map priorities.",
+      "output": "Agents (HIGH): Probability_Expert, TheoreticalChemistry_Expert, Verification_Expert. Tool (MEDIUM): Computer_terminal. Input (HIGH): Riddle / User Query. Output (HIGH): Recommended Ball. Human (HIGH): End User. Tasks (HIGH): Statistical Simulation & Analysis; Chemical/Theoretical Modeling; Verification & Recommendation."
+    },
+    {
+      "explanation": "4) Map relations (PERFORMS, NEXT, CONSUMED_BY, PRODUCES, DELIVERS_TO, USES). Verify chain Input→Agent→Task→Output→Human.",
+      "output": "Input consumed by Probability_Expert who performs task_001; task_001 NEXT task_002; task_002 NEXT task_003; task_003 produces output which is delivered to the end user. Probability_Expert uses Computer_terminal for simulation."
+    },
+    {
+      "explanation": "5) Identify failures & optimizations from trace metadata and interaction evidence.",
+      "output": "Detected an execution error in the Probability_Expert simulation implementation (metadata.mistake_agent). Detection gap in verification stage. Recommendations: add deterministic test cases, unit tests, and an automated verification step; improve tool integration for reproducible simulation runs."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Cross-Disciplinary Probability & Theoretical Chemistry Riddle Solver",
+    "system_summary": "A three-stage interdisciplinary workflow combining statistical simulation, theoretical modeling, and verification to identify the ping-pong ball with maximum ejection probability. Probability and theoretical chemistry experts collaborate using a computer terminal to simulate the game mechanics, then a verification expert validates results and issues the final recommendation to the end user.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Probability Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 40
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Theoretical Chemistry Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 80
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 120
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer Terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 200
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Statistical Simulation & Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 60,
+            "line_end": 160
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Chemical/Theoretical Modeling of Game Mechanics",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 120
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Solution Verification & Recommendation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 120,
+            "line_end": 220
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Riddle: Pick That Ping-Pong (100-ball game description)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 40
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Recommended Ball Number (max ejection probability)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 180,
+            "line_end": 220
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "End User / Contestant",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 10
+          }
+        ]
+      }
+    ],
+    "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": 40
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 60,
+            "line_end": 160
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 120
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 120,
+            "line_end": 220
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 60,
+            "line_end": 160
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 120,
+            "line_end": 220
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 180,
+            "line_end": 220
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 180,
+            "line_end": 220
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 140,
+            "line_end": 200
+          }
+        ]
+      },
+      {
+        "id": "rel_010",
+        "source": "tool_001",
+        "target": "task_001",
+        "type": "REQUIRED_BY",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 140,
+            "line_end": 200
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Probability_Expert made an error in the simulation implementation that produced an incorrect outcome.",
+        "raw_text": "The agent made an error in the simulation implementation, resulting in an incorrect outcome.",
+        "raw_text_ref": [
+          {
+            "line_start": 1,
+            "line_end": 30
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Verification stage did not detect the inconsistency between the simulation result and the declared ground truth.",
+        "raw_text": "metadata.mistake_step = 1; ground_truth = 3; is_correct = false",
+        "raw_text_ref": [
+          {
+            "line_start": 1,
+            "line_end": 30
+          }
+        ],
+        "affected_id": "agent_003"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Add deterministic unit tests, reproducible seeds, and small-case analytical checks to the Probability_Expert's simulation prompt and implementation to catch implementation errors early.",
+        "affected_ids": [
+          "agent_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 60,
+            "line_end": 160
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enhance the Computer Terminal workflow with automated verification hooks (compare simulation outputs to analytical baselines and ground truth) and explicit cross-agent validation before final recommendation.",
+        "affected_ids": [
+          "tool_001",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 120,
+            "line_end": 220
+          }
+        ]
+      }
+    ]
+  },
+  "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_191442_2306ed65.json

@@ -0,0 +1,369 @@
+{
+  "timestamp": "20250907_191442",
+  "extraction_id": "2306ed65",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Count distinct agents and classify tools: From data.agents there are four participants, but 'Computer_terminal' matches the Computer* pattern and is classified as a Tool. Distinct Agents: ArithmeticProgressions_Expert, ProblemSolving_Expert, Verification_Expert (3 agents).",
+      "output": ""
+    },
+    {
+      "explanation": "Determine workflow type and main goal: Trace title, description, and content contain 'How much did I save', 'cost', 'season pass', 'daily tickets', and 'verification' → SIMPLE VERIFICATION workflow (single verification task).",
+      "output": ""
+    },
+    {
+      "explanation": "Extract entities: create Agent entities for the three specialist roles, a Tool entity for Computer_terminal, one Task (verification of costs/savings), one Input (user question), one Output (verified costs and savings), and one Human (end user receiving results).",
+      "output": ""
+    },
+    {
+      "explanation": "Map relations: Input is CONSUMED_BY agents; each Agent PERFORMS the single verification task; Task PRODUCES the Output; Output DELIVERS_TO the Human; Agents USE the Computer_terminal tool (supporting resource).",
+      "output": ""
+    },
+    {
+      "explanation": "Identify failures and optimizations: metadata indicates a mistake by Verification_Expert failing to collect price data (retrieval error). Also observed reliance on historical ranges rather than authoritative sources (hallucination/risk). Recommend enabling authoritative price retrieval and clarifying agent responsibilities.",
+      "output": ""
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Season-Pass Savings Verification System",
+    "system_summary": "A simple multi-agent verification workflow to verify ticket and season-pass prices and compute savings for planned visits. Three domain experts collaborate (arithmetic, problem solving, verification) with a Computer terminal tool for coordination; the primary goal is to verify costs and report savings.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "ArithmeticProgressions_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 55,
+            "line_end": 70
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "ProblemSolving_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 19,
+            "line_end": 45
+          },
+          {
+            "line_start": 80,
+            "line_end": 88
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 46,
+            "line_end": 54
+          },
+          {
+            "line_start": 71,
+            "line_end": 79
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Verify Ticket and Season-Pass Pricing & Compute Savings",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Question: season pass vs daily tickets (summer 2024 visits)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified costs and computed savings (amount saved)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 19,
+            "line_end": 45
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "End User / Question Asker",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 3
+          }
+        ]
+      }
+    ],
+    "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": 18
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "input_001",
+        "target": "agent_002",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "input_001",
+        "target": "agent_003",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 19,
+            "line_end": 45
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 55,
+            "line_end": 70
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 19,
+            "line_end": 45
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 19,
+            "line_end": 45
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 19,
+            "line_end": 45
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 55,
+            "line_end": 70
+          }
+        ]
+      },
+      {
+        "id": "rel_010",
+        "source": "agent_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "rel_011",
+        "source": "agent_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 46,
+            "line_end": 54
+          },
+          {
+            "line_start": 71,
+            "line_end": 79
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Verification_Expert failed to collect authoritative price data for daily tickets and season passes for California's Great America in 2024.",
+        "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": 5,
+            "line_end": 6
+          }
+        ],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "HALLUCINATION",
+        "description": "Verification relied on historical ranges and plausibility rather than authoritative sources, introducing potential incorrect verification.",
+        "raw_text": "Since I am currently unable to access external websites, I will use the provided cost, and verify against known patterns or typical cost adjustments from previous years.",
+        "raw_text_ref": [
+          {
+            "line_start": 19,
+            "line_end": 28
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enable authoritative price retrieval by granting a designated agent (Verification_Expert or a dedicated Retrieval agent) access to a price API or web lookup tool so prices can be fetched rather than inferred.",
+        "affected_ids": [
+          "agent_003",
+          "tool_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 19,
+            "line_end": 28
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine the manager plan to explicitly assign authoritative-data collection to a specific agent and require citation of sources for price verification, avoiding reliance on historical ranges.",
+        "affected_ids": [
+          "agent_002",
+          "agent_003",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 1,
+            "line_end": 18
+          }
+        ]
+      }
+    ]
+  },
+  "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_191642_95f03b92.json

@@ -0,0 +1,377 @@
+{
+  "timestamp": "20250907_191642",
+  "extraction_id": "95f03b92",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON STRUCTURE ANALYSIS: Count distinct agents and identify domain & main goal.",
+      "output": "Detected 4 distinct agents (Computer_terminal, DataVerification_Expert, Eateries_Expert, Location-Based_Services_Expert). Domain: location_based_services. Main goal: location-based discovery — find the closest eatery to Harkness Memorial State Park open at 11pm on Wednesdays."
+    },
+    {
+      "explanation": "2) Workflow classification & task generation using gold-standard patterns.",
+      "output": "Classified as COMPLEX SEQUENTIAL WORKFLOW (DISCOVERY). Generated 3 high-level tasks: Geographic Proximity Analysis, Restaurant Data Collection, Operating Hours Validation."
+    },
+    {
+      "explanation": "3) Entity extraction and relation mapping.",
+      "output": "Mapped agents to tasks (Location-Based_Services_Expert -> Geographic analysis; Eateries_Expert -> Data collection; DataVerification_Expert -> Hours validation). Computer_terminal classified as Tool. Input is the user query; Output is restaurant recommendation delivered to end user. Created PERFORMS, NEXT, CONSUMED_BY, PRODUCES, DELIVERS_TO, and USES relations."
+    },
+    {
+      "explanation": "4) Quality check, failures and optimizations.",
+      "output": "Found two failures (DataVerification execution error due to broken web-search code; retrieval coverage issue resulting in no matching eateries). Proposed tool and prompt refinements to improve robustness and coverage."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Location-Based Restaurant Discovery System",
+    "system_summary": "Multi-agent location-based discovery pipeline that finds the closest eatery to a given park meeting time constraints. The workflow uses a Location-Based Services expert to locate and rank nearby venues, an Eateries expert to collect candidate details, and a Data Verification expert to validate operating hours; a Computer Terminal tool is used for web/search execution.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Location-Based Services Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Eateries Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Data 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": "Geographic Proximity Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Restaurant Data Collection",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Operating Hours Validation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Restaurant Query",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Restaurant Recommendations",
+        "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_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
+          }
+        ]
+      },
+      {
+        "id": "rel_011",
+        "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": "EXECUTION_ERROR",
+        "description": "DataVerification_Expert's web-search code returned None causing a TypeError during operating-hours verification.",
+        "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": "Restaurant Data Collection failed to find any eateries open until 11 PM on Wednesdays within searched sources/radius, causing no qualifying candidates.",
+        "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": "agent_002"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Harden the web/search tool: ensure perform_web_search returns deterministic list objects (empty list on no results), add retries, timeouts, and structured results (source + snippet). This prevents NoneType iteration errors and improves reliability for Data Verification.",
+        "affected_ids": [
+          "tool_001",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine the data collection & search prompts to include authoritative APIs (e.g., Google Places/Maps, official venue pages), explicit fallback radius expansion, and a prioritized source list. This increases recall for late-night venues and reduces retrieval failures.",
+        "affected_ids": [
+          "task_002",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 29909,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_1.json\",\n  \"title\": \"Algorithm Sample 1: What is the closest eatery to Harkness Memorial State Park t...\",\n  \"description\": \"Complex location-based services sample with 4 specialized agents. Involves geographic queries, time-based filtering, and data verification.\",\n  \"trace_type\": \"location_based_services\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"location_services\",\n    \"data_verification\",\n    \"re..."
+}

extraction_analysis/cot_extraction_20250907_191752_441b58eb.json

@@ -0,0 +1,358 @@
+{
+  "timestamp": "20250907_191752",
+  "extraction_id": "441b58eb",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) Count distinct agents and identify domain & goal from trace.",
+      "output": "Agents detected in content.data.agents: Computer_terminal, Probability_Expert, TheoreticalChemistry_Expert, Verification_Expert. Computer_terminal is classified as a Tool. Domain: cross-disciplinary probability & theoretical chemistry. Main goal: identify which ping-pong ball maximizes ejection probability (interdisciplinary analysis + simulation + verification)."
+    },
+    {
+      "explanation": "2) Decide workflow pattern and tasks using the Gold standard mappings.",
+      "output": "This trace matches INTERDISCIPLINARY_ANALYSIS (probability + theoretical chemistry). Generate 3 sequential tasks: (1) Mechanics modeling & plan, (2) Statistical simulation execution, (3) Result verification and consensus."
+    },
+    {
+      "explanation": "3) Extract entities and map relations (PERFORMS, NEXT, USES, CONSUMED_BY, PRODUCES, DELIVERS_TO).",
+      "output": "Mapped 3 expert agents, 1 tool, 3 tasks, 1 input, 1 output, 1 human. Included 2 failures (simulation execution error, premature consensus) and 2 optimizations (test harness & verification protocol)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Cross-Disciplinary Simulation & Verification System for 'Pick That Ping-Pong'",
+    "system_summary": "A three-expert multi-step workflow where theoretical modeling informs a probabilistic simulation executed on a Computer terminal, followed by independent verification to produce a recommended ball selection. The goal is to determine which numbered ping-pong ball maximizes the probability of ejection.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Theoretical Chemistry Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 40
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Probability Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 41,
+            "line_end": 90
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 101,
+            "line_end": 140
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer Terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 91,
+            "line_end": 100
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Mechanics Modeling and Plan",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 40
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Statistical Simulation Execution",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 41,
+            "line_end": 100
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Result Verification and Consensus",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 101,
+            "line_end": 140
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Pick That Ping-Pong Riddle (problem statement)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 20
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Recommended Ball Selection (simulation result: ball 2)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 91,
+            "line_end": 100
+          },
+          {
+            "line_start": 101,
+            "line_end": 120
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Game Player / End User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 5
+          }
+        ]
+      }
+    ],
+    "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": 20
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 40
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 41,
+            "line_end": 90
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 101,
+            "line_end": 140
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 90
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 41,
+            "line_end": 140
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 101,
+            "line_end": 120
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 101,
+            "line_end": 120
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 41,
+            "line_end": 100
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Probability_Expert made an implementation error in the simulation resulting in an incorrect reported outcome (simulation returned ball 2 while ground truth indicates 3).",
+        "raw_text": "metadata.mistake_reason: 'The agent made an error in the simulation implementation, resulting in an incorrect outcome.'; Computer_terminal output: 'The ball you should pick to maximize your odds of winning is: 2'.",
+        "raw_text_ref": [
+          {
+            "line_start": 141,
+            "line_end": 145
+          },
+          {
+            "line_start": 91,
+            "line_end": 100
+          }
+        ],
+        "affected_id": "agent_002"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Verification step accepted the simulation result and terminated consensus without an independent replication or in-depth code review, allowing the incorrect result to be propagated.",
+        "raw_text": "Verification_Expert: '...do you agree... If both confirm, I will conclude the task.' Followed by 'TERMINATE' despite metadata indicating a mistake_step.",
+        "raw_text_ref": [
+          {
+            "line_start": 101,
+            "line_end": 120
+          },
+          {
+            "line_start": 141,
+            "line_end": 145
+          }
+        ],
+        "affected_id": "agent_003"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Introduce an automated deterministic simulation harness and unit tests in the Computer Terminal environment (seed control, edge-case tests, and ejection-tracking assertions) to catch implementation errors before accepting results.",
+        "affected_ids": [
+          "tool_001",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 41,
+            "line_end": 100
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require an explicit independent replication step and structured verification protocol: another agent must re-run or review the simulation and validate results against analytical reasoning before the workflow can terminate.",
+        "affected_ids": [
+          "agent_003",
+          "agent_001",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 101,
+            "line_end": 140
+          }
+        ]
+      }
+    ]
+  },
+  "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_191857_956f6be1.json

@@ -0,0 +1,308 @@
+{
+  "timestamp": "20250907_191857",
+  "extraction_id": "956f6be1",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Count distinct agents and identify tool-like components.",
+      "output": "Detected participants: ArithmeticProgressions_Expert, ProblemSolving_Expert, Verification_Expert (these match *_Expert pattern). Computer_terminal is present but classified as a Tool (Computer* -> Tool). Total expert agents = 3; total participants including tool = 4."
+    },
+    {
+      "explanation": "Determine domain and workflow structure from trace text.",
+      "output": "Trace contains 'Verify', 'cost', 'season pass', 'savings' and a direct question asking 'How much did I save...'. This matches the SIMPLE VERIFICATION pattern. Use a single consolidated verification task performed by multiple experts."
+    },
+    {
+      "explanation": "Extract entities (Agents, Tool, Input, Output, Human) and map to IDs.",
+      "output": "Agents: ProblemSolving_Expert (agent_001), ArithmeticProgressions_Expert (agent_002), Verification_Expert (agent_003). Tool: Computer_terminal (tool_001). Input: User question (input_001). Task: Unified verification and savings computation (task_001). Output: Verified savings report (output_001). Human: End User (human_001)."
+    },
+    {
+      "explanation": "Build relations according to CORE rules: PERFORMS for agents→task, CONSUMED_BY from Input→agent, PRODUCES from task→output, DELIVERS_TO from output→human, USES for tool dependencies.",
+      "output": "Created PERFORMS relations for all three experts to the single task. Input consumed by the primary problem-solving agent. Task produces the verified report. Output delivered to the end user. ProblemSolving_Expert and Verification_Expert linked to Computer_terminal via USES (medium importance)."
+    },
+    {
+      "explanation": "Identify failures and optimizations from trace metadata and observations.",
+      "output": "Failure: Verification_Expert failed to collect price data (metadata.mistake_reason). Optimizations: enable reliable external price retrieval for tool and refine verification prompt/steps to ensure data collection is explicit."
+    },
+    {
+      "explanation": "Quality checks.",
+      "output": "All relations reference existing entity IDs. Workflow covers Input→Agent→Task→Output→Human. Single-task verification structure chosen per Gold standard rules."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Season-Pass Savings Verification System",
+    "system_summary": "A simple multi-expert verification workflow to confirm ticket and season-pass prices for California's Great America (summer 2024) and compute the resulting savings. Three expert agents collaborate on a single verification-and-calculation task, using a computer terminal tool for support.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "ProblemSolving_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "ArithmeticProgressions_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Verify Ticket and Season-Pass Costs and Compute Savings (Summer 2024)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Question: Season-pass vs daily tickets (4 summer visits in 2024)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified costs and computed amount saved",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "End User",
+        "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": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "agent_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Verification_Expert failed to collect price data for daily tickets and season passes for California's Great America in 2024.",
+        "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"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enable Computer_terminal to access authoritative external pricing sources (official park website/API) or integrate a price-retrieval API so Verification_Expert can fetch current 2024 prices programmatically, reducing manual inference.",
+        "affected_ids": [
+          "tool_001",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine the verification task prompt to explicitly require citation of source(s) and step-by-step evidence for price confirmation (e.g., 'Provide source URL and retrieval timestamp for each price'), to prevent plausible but unsupported inferences.",
+        "affected_ids": [
+          "task_001",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 1,
+            "line_end": 2
+          }
+        ]
+      }
+    ]
+  },
+  "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_192012_b597400d.json

@@ -0,0 +1,346 @@
+{
+  "timestamp": "20250907_192012",
+  "extraction_id": "b597400d",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Count distinct agents and identify workflow type from observations and trace metadata.",
+      "output": "1 distinct agent (single RAG-powered Python assistant). Workflow is a multi-step documentation/education process (knowledge retrieval → answer synthesis → example generation)."
+    },
+    {
+      "explanation": "Extract entities (Agents, Tasks, Tools, Inputs/Outputs, Human) following the Gold-standard mapping for a 3-task sequential workflow.",
+      "output": "Identified 1 Agent, 3 Tasks (sequential), 2 Tools, 1 Input, 1 Output, 1 Human."
+    },
+    {
+      "explanation": "Map relations: PERFORMS for agent→tasks, NEXT between tasks, CONSUMED_BY/PRODUCES/DELIVERS_TO for flow, USES for tool dependencies. Add 1-2 failures and optimization suggestions derived from trace metadata.",
+      "output": "Constructed relations linking Input→Agent→Task_1→Task_2→Task_3→Output→Human, plus USES links to tools. Added failures about missing explicit agent label and single-agent bottleneck; optimizations: prompt refinement and caching."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "RAG-Powered Python Documentation Assistant",
+    "system_summary": "A single RAG-enabled Python documentation assistant that performs a three-step sequential workflow: retrieve relevant documentation, synthesize concise explanations, and produce validated code examples for learners.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Python Documentation Assistant",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 17,
+            "line_end": 20
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Documentation Retrieval Service",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 9,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "id": "tool_002",
+        "type": "Tool",
+        "name": "LLM Inference Engine (gpt-4o-2024-11-20)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 24
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Documentation Retrieval",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 9,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Answer Synthesis & Explanation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 17,
+            "line_end": 26
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Example Generation & Validation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 37,
+            "line_end": 46
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Python Query",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Explained Answer with Code Examples",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 37,
+            "line_end": 46
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Learner (End User)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 3
+          }
+        ]
+      }
+    ],
+    "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": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 9,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_001",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 17,
+            "line_end": 26
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_001",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 37,
+            "line_end": 46
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 9,
+            "line_end": 26
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 17,
+            "line_end": 46
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 37,
+            "line_end": 46
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 37,
+            "line_end": 46
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 9,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "id": "rel_010",
+        "source": "agent_001",
+        "target": "tool_002",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 24
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Component hierarchy lists an empty agent name, indicating the agent role/prompt is not explicitly recorded in the trace.",
+        "raw_text": "\"agents\": [ \"\" ]",
+        "raw_text_ref": [
+          {
+            "line_start": 53,
+            "line_end": 56
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Single-agent architecture (agent_count = 1) may create a performance or scaling bottleneck for heavier workloads.",
+        "raw_text": "\"agent_count\": 1",
+        "raw_text_ref": [
+          {
+            "line_start": 53,
+            "line_end": 56
+          }
+        ],
+        "affected_id": "agent_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Explicitly record and surface the assistant's system prompt and role (e.g., separate 'Retriever' and 'Synthesizer' role prompts) so provenance and responsibilities are clear.",
+        "affected_ids": [
+          "agent_001",
+          "task_001",
+          "task_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 17,
+            "line_end": 26
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Introduce caching for retrieved documentation results to reduce search latency (observed search_time_ms and sequential calls) and lower repeated retrieval costs.",
+        "affected_ids": [
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 9,
+            "line_end": 12
+          }
+        ]
+      }
+    ]
+  },
+  "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   ..."
+}