add

agentgraph/methods/production/openai_structured_extractor.py

@@ -163,29 +163,43 @@ ANALYSIS STEPS:
    - Input/Output: Single workflow start/end points
    - Human: End users receiving outputs
 
-3. WORKFLOW CLASSIFICATION & TASK GENERATION:
+3. WORKFLOW CLASSIFICATION & TASK GENERATION (Multi-Agent Best Practices):
    - 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, ONLY ONE lead agent PERFORMS it (others collaborate via different relations)
-     * DISCOVERY: 3 sequential tasks with NEXT relations (each agent performs their specialized task)
-     * INTERDISCIPLINARY: 3 domain tasks with NEXT relations (each agent performs their specialized task)
+     * Contains "cost", "savings", "ticket", "verification" → VERIFICATION (3 specialized tasks)
+     * Contains "location", "restaurant", "proximity", "search" → DISCOVERY (3 sequential tasks)  
+     * Contains "probability", "game theory", "chemistry" → INTERDISCIPLINARY (3 domain tasks)
+     * Simple single-agent scenarios → SIMPLE (1 agent, 1 task)
    
-   CRITICAL: 
-   * VERIFICATION workflows = 1 PERFORMS relation (collaborative model)
-   * SIMPLE DOCUMENTATION/QA = 1 agent, 1 task, 1 PERFORMS (avoid over-decomposition)
-   * COMPLEX MULTI-STEP = 3 agents, 3 tasks, 3 PERFORMS (specialized pipeline)
-
-4. RELATION MAPPING (KnowPrompt-Enhanced):
-   - PERFORMS: 
-     * VERIFICATION workflows: 1 PERFORMS only (lead expert performs, others support via INTERVENES/USES)
-     * DISCOVERY/INTERDISCIPLINARY: 3 PERFORMS (1:1 agent-task mapping)
-   - NEXT: Use only for multi-task workflows (task_001→task_002→task_003)
-   - CONSUMED_BY/PRODUCES/DELIVERS_TO: Standard workflow flow (Input→Agent→Task→Output→Human)
-   - USES/REQUIRED_BY: Tool connections and agent collaborations
-   - INTERVENES: Supporting agents in collaborative workflows (VERIFICATION pattern)
+   - GENERATE tasks accordingly (Independent Task Allocation):
+     * VERIFICATION: 3 specialized verification tasks 
+       Example: "Cost Data Analysis" → "Savings Calculation Verification" → "Final Report Generation"
+     * DISCOVERY: 3 sequential discovery tasks 
+       Example: "Geographic Analysis" → "Data Collection" → "Results Validation"
+     * INTERDISCIPLINARY: 3 domain tasks 
+       Example: "Statistical Analysis" → "Chemical Modeling" → "Solution Integration"
+     * SIMPLE: 1 unified task for single-agent workflows
+   
+   CRITICAL PRINCIPLE: Each Agent = Independent Task (避免职责重叠)
+   * Multi-agent workflows: N agents → N tasks → N PERFORMS (1:1:1 mapping)
+   * Clear responsibility boundaries prevent "全连接混乱"  
+   * Parallel task execution improves transparency and efficiency
+   
+   MANDATORY RULE: NO TASK SHARING
+   * NEVER assign multiple agents to the same task
+   * Each task must have exactly ONE agent performing it
+   * Use task decomposition instead of agent collaboration on single tasks
+
+4. RELATION MAPPING (Strict 1:1 Task Assignment):
+   - PERFORMS: EXACTLY one agent per task (no sharing, no collaboration on same task)
+     * VERIFICATION: agent_001→task_001, agent_002→task_002, agent_003→task_003
+     * DISCOVERY: agent_001→task_001, agent_002→task_002, agent_003→task_003  
+     * INTERDISCIPLINARY: agent_001→task_001, agent_002→task_002, agent_003→task_003
+     * SIMPLE: agent_001→task_001
+   
+   - NEXT: Sequential task dependencies (task_001→task_002→task_003)
+   - CONSUMED_BY/PRODUCES/DELIVERS_TO: Standard workflow flow
+   - USES/REQUIRED_BY: Tool and support connections only
+   - ABSOLUTE RULE: Each task has EXACTLY ONE performer - no exceptions!
 
 5. QUALITY CHECK (Contextual Graph Enhanced):
    - Verify all relation IDs reference existing entities

extraction_analysis/cot_extraction_20250907_200318_63bf8e33.json

@@ -0,0 +1,357 @@
+{
+  "timestamp": "20250907_200318",
+  "extraction_id": "63bf8e33",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) Count distinct agents: from data.agents and observations, identify *_Expert patterns and exclude Computer_terminal → ArithmeticProgressions_Expert, ProblemSolving_Expert, Verification_Expert (3 agents). 2) Classify workflow: trace contains 'cost', 'savings', 'ticket', 'verification' → VERIFICATION workflow. 3) Apply Gold-standard mapping for verification: use a 3-task structure (Cost data confirmation, Savings calculation, Final verification/reporting). 4) Map agents to independent tasks (1:1): ProblemSolving_Expert → Cost data confirmation; ArithmeticProgressions_Expert → Savings calculation; Verification_Expert → Final verification & reporting. 5) Identify Computer_terminal as a Tool used during the run. 6) Extract Input (user query) and Output (savings result) and map relations (CONSUMED_BY, PERFORMS, NEXT, PRODUCES, DELIVERS_TO, USES). 7) Locate failures in metadata and observations and propose optimizations.",
+      "output": ""
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Season Pass Savings Verification System",
+    "system_summary": "A multi-agent verification workflow that confirms ticket and season-pass costs, computes savings for planned visits, and validates results. Three specialist agents perform cost confirmation, arithmetic calculation, and final verification; a Computer_terminal tool mediates chat/operations.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "ArithmeticProgressions_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "ProblemSolving_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 6,
+            "line_end": 7
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Cost Data Confirmation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Savings Calculation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Final Verification & Report Generation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 6,
+            "line_end": 7
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Ticket Savings Query",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Reported 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_002",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_001",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 6,
+            "line_end": 7
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Verification_Expert did not collect authoritative price data and instead relied on provided values.",
+        "raw_text": "For this, I will need to check the official website ... however, since I am currently unable to access external websites, I will use the provided cost",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Final reported savings are incorrect according to trace metadata (ground_truth $55); error stems from missing price retrieval.",
+        "raw_text": "mistake_reason: The agent fails to collect price data for the daily tickets and season passes for California's Great America in 2024.",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "output_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Add an explicit retrieval-and-citation requirement to the Verification_Expert prompt: require fetching authoritative price sources (URLs or citations) before accepting any provided values.",
+        "affected_ids": [
+          "agent_003",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enable or integrate a dedicated data-retrieval tool (or permit Computer_terminal web access) so agents can programmatically confirm current prices, reducing reliance on assumed/historical ranges.",
+        "affected_ids": [
+          "tool_001",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      }
+    ]
+  },
+  "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_200510_246354f7.json

@@ -0,0 +1,337 @@
+{
+  "timestamp": "20250907_200510",
+  "extraction_id": "246354f7",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Count distinct agents and inspect trace structure.",
+      "output": "Agents detected in data.agents: ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert (4 distinct). Computer_terminal is a system/tool (Computer*). Domain: cost verification (season pass vs daily tickets). Main goal: verify costs and compute savings for visits in summer 2024."
+    },
+    {
+      "explanation": "Determine workflow type using gold-standard patterns.",
+      "output": "This is a SIMPLE VERIFICATION workflow (costs/calculations). Follow the pattern: one consolidated verification task with multiple collaborating experts."
+    },
+    {
+      "explanation": "Extract entities (Agents, Tool, Task, IO, Human) and map references to observations.",
+      "output": "Agents (3 experts) + 1 Tool (Computer_terminal), 1 Task, 1 Input, 1 Output, 1 Human. References mapped to observations: ProblemSolving_Expert (obs line 1), Verification_Expert (obs lines 2,6,7), Computer_terminal (obs lines 3,5), ArithmeticProgressions_Expert (obs line 4)."
+    },
+    {
+      "explanation": "Assign relations following Independent Task Allocation for a simple verification workflow.",
+      "output": "Input -> consumed by experts; all experts perform the single verification task; task produces an output delivered to the end user. Computer_terminal is a supporting tool used by agents. No NEXT relations (single task)."
+    },
+    {
+      "explanation": "Identify failures and optimizations from trace metadata and observations.",
+      "output": "Failure: Verification_Expert failed to retrieve/collect price data (trace metadata & observation text). Optimizations: add an external data retrieval tool or change Verification_Expert prompt to mandate explicit data sourcing and verification steps."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Season Pass Savings Verification System",
+    "system_summary": "A multi-agent verification workflow to confirm 2024 ticket and season-pass prices for California's Great America and compute savings for planned visits. Three human-role experts collaboratively verify prices and compute savings, supported by a computer terminal tool used for coordination.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "ArithmeticProgressions_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "ProblemSolving_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 6,
+            "line_end": 7
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Season Pass Savings Verification",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Season Pass Savings Query (summer 2024 visits)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified Savings Result (daily ticket price, season pass price, amount saved)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "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_002",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 6,
+            "line_end": 7
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "agent_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "agent_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Verification_Expert failed to collect authoritative 2024 price data (unable to access external sources), causing reliance on provided values.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ],
+        "affected_id": "agent_003"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Final correctness flagged as incorrect in trace metadata (ground truth $55), indicating end-to-end verification produced an incorrect result.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Integrate an external data-retrieval tool or API for authoritative ticket pricing (e.g., official park pricing API or web-scraper service) so Verification_Expert can fetch live 2024 prices instead of relying on historical patterns.",
+        "affected_ids": [
+          "agent_003",
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine Verification_Expert's prompt to require explicit data sourcing steps, citation of source URLs, and an explicit retrieval-check step before using provided values.",
+        "affected_ids": [
+          "agent_003",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 2,
+            "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_200558_dd180f3b.json

@@ -0,0 +1,381 @@
+{
+  "timestamp": "20250907_200558",
+  "extraction_id": "dd180f3b",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON STRUCTURE ANALYSIS: count agents, identify domain and main goal.",
+      "output": "Distinct agents found (in content.data.agents / component_hierarchy): 3 human agents: Location-Based_Services_Expert, Eateries_Expert, DataVerification_Expert. One tool: Computer_terminal. Domain: location_based_services. MAIN GOAL: complex location-based discovery: find the closest eatery to Harkness Memorial State Park that is open at 11pm on Wednesdays."
+    },
+    {
+      "explanation": "2) Decide workflow pattern and task structure using Gold-standard patterns.",
+      "output": "Workflow classified as COMPLEX SEQUENTIAL WORKFLOW (DISCOVERY). Generate 3 sequential tasks (Geographic Analysis → Data Collection → Operating Hours Validation). Map each of the 3 specialist agents to one independent task (1:1 mapping). Computer_terminal is a Tool used by agents for web/search and execution."
+    },
+    {
+      "explanation": "3) Extract entities (Agents, Tasks, Tool, Input/Output, Human) and assign IDs.",
+      "output": "Entities created: 3 Agents (agent_001..agent_003), 3 Tasks (task_001..task_003), 1 Tool (tool_001), 1 Input (input_001), 1 Output (output_001), 1 Human (human_001). raw_prompt fields left empty; raw_prompt_ref and interaction ref point to trace regions where roles, question, and executions are present."
+    },
+    {
+      "explanation": "4) Map relations: PERFORMS, NEXT, CONSUMED_BY/PRODUCES/DELIVERS_TO, USES.",
+      "output": "Relations created: input consumed by Location-Based Services Expert, PERFORMS for each agent→task (1:1), NEXT links task_001→task_002 and task_002→task_003, task_003 PRODUCES output, output DELIVERS_TO human, agents USE tool."
+    },
+    {
+      "explanation": "5) Quality check, failures and optimizations.",
+      "output": "Verified all relation sources and targets reference existing entities. Identified a documented execution failure by DataVerification_Expert (Python code returned None / raised TypeError). Suggested optimizations to improve the web search wrapper, add error handling/retries and multi-source validation."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Location-Based Restaurant Discovery System",
+    "system_summary": "Multi-agent location-based discovery workflow to find the closest eatery to Harkness Memorial State Park open at 11pm on Wednesdays. The system uses a Location-Based Services Expert for geographic search, an Eateries Expert for initial eatery identification, and a Data Verification Expert to validate operating hours, supported by a Computer Terminal tool for web/search and execution.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Location-Based Services Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 15,
+            "line_end": 35
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Eateries Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 14
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Data Verification Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 80,
+            "line_end": 120
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer Terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 45,
+            "line_end": 80
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Geographic Proximity Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 15,
+            "line_end": 25
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Restaurant Data Collection",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 25,
+            "line_end": 45
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Operating Hours Validation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 45,
+            "line_end": 85
+          }
+        ]
+      },
+      {
+        "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": "Closest Eatery Recommendation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 90,
+            "line_end": 100
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "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": 15,
+            "line_end": 25
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 15,
+            "line_end": 35
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 20
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 80,
+            "line_end": 120
+          }
+        ]
+      },
+      {
+        "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": 100
+          }
+        ]
+      },
+      {
+        "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
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 30,
+            "line_end": 45
+          }
+        ]
+      },
+      {
+        "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": 80,
+            "line_end": 110
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "DataVerification_Expert execution failed due to an exception in the web-search / parsing code (perform_web_search returned None causing TypeError).",
+        "raw_text": "mistake_agent: DataVerification_Expert; mistake_reason: The Python code is incorrect. TypeError: 'NoneType' object is not iterable",
+        "raw_text_ref": [
+          {
+            "line_start": 12,
+            "line_end": 14
+          },
+          {
+            "line_start": 80,
+            "line_end": 88
+          }
+        ],
+        "affected_id": "agent_003"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Harden the perform_web_search wrapper: return an empty iterable instead of None on failures, add retries/backoff, validate return types, and surface structured results for downstream validation. This reduces EXECUTION_ERROR risk for DataVerification_Expert.",
+        "affected_ids": [
+          "tool_001",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 80,
+            "line_end": 88
+          },
+          {
+            "line_start": 45,
+            "line_end": 50
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Cache and aggregate hours data from multiple authoritative sources (official restaurant pages, Google Places, Yelp) before validation to reduce repeated web calls and false negatives. Also add a fallback step to expand search radius and present 'no matches' with nearby late-night alternatives.",
+        "affected_ids": [
+          "agent_002",
+          "agent_001",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 25,
+            "line_end": 45
+          },
+          {
+            "line_start": 60,
+            "line_end": 80
+          }
+        ]
+      }
+    ]
+  },
+  "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_200701_13d69845.json

@@ -0,0 +1,319 @@
+{
+  "timestamp": "20250907_200701",
+  "extraction_id": "13d69845",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON STRUCTURE ANALYSIS: Count distinct agents from data/observations. Identified agents: Probability_Expert, TheoreticalChemistry_Expert, Verification_Expert, plus a Computer_terminal (Computer* → Tool). Domain: cross-disciplinary (probability + theoretical chemistry). Main goal: determine which ping-pong ball maximizes ejection probability (single decision derived from multi-domain analysis).",
+      "output": ""
+    },
+    {
+      "explanation": "2) WORKFLOW CLASSIFICATION: Trace contains 'probability' and 'theoretical_chemistry' tags and cross-disciplinary discussion. According to the Gold standard, classify as INTERDISCIPLINARY_ANALYSIS and generate 3 domain-specific tasks. Map each specialist agent to a single independent task (1:1 mapping).",
+      "output": ""
+    },
+    {
+      "explanation": "3) ENTITY & RELATION MAPPING: Create entities for 3 Agents, 3 Tasks, 1 Tool, 1 Input, 1 Output, 1 Human. Assign PERFORMS relations (each agent→its task). Link tasks sequentially using NEXT (task_001 → task_002 → task_003). Connect Input→Agent (CONS UMED_BY), Task→Output (PRODUCES), Output→Human (DELIVERS_TO). Record USES relations for Tool dependencies.",
+      "output": ""
+    },
+    {
+      "explanation": "4) QUALITY CHECK & RISKS: Add failures found in trace metadata (mistake_agent = Probability_Expert; execution error in simulation). Add secondary planning/reproducibility risk. Propose optimizations: deterministic seeding / larger iterations and clearer cross-validation by Verification_Expert.",
+      "output": ""
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Cross-Disciplinary Riddle Solver (Probability + Theoretical Chemistry)",
+    "system_summary": "A three-agent interdisciplinary workflow that simulates and models a stochastic game-show device to identify the ping-pong ball with highest ejection probability. Probability and theoretical-chemistry experts produce analyses, and a verification expert validates and synthesizes the final recommendation; a Computer_terminal runs the simulation.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Probability_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "TheoreticalChemistry_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Statistical Simulation & Probability Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Theoretical Mechanistic Modeling",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Verification, Aggregation & Result Synthesis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Game Riddle Description (100-ball ramp & piston rules)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Recommended Ball Selection (number)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Contestant / End User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      },
+      {
+        "id": "rel_010",
+        "source": "agent_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": []
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Probability_Expert made an implementation error in the simulation that produced an incorrect outcome (recorded in trace metadata).",
+        "raw_text": "mistake_agent: Probability_Expert; mistake_reason: The agent made an error in the simulation implementation, resulting in an incorrect outcome.",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Reproducibility and robustness risk: random seed control and iteration planning not enforced (may affect result stability).",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "task_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Run simulations with deterministic seeding, increase iteration count, and add statistical convergence checks to the Computer_terminal simulation pipeline to reduce execution errors and variance.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "WORKFLOW_SIMPLIFICATION",
+        "description": "Introduce an explicit independent replication and cross-validation step where Verification_Expert re-runs simulations and compares results before final synthesis (improves detection of implementation mistakes).",
+        "affected_ids": [
+          "agent_003",
+          "task_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_200747_8e1b7220.json

@@ -0,0 +1,244 @@
+{
+  "timestamp": "20250907_200747",
+  "extraction_id": "8e1b7220",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON STRUCTURE ANALYSIS: Count distinct agents and identify workflow type from observations and metadata.",
+      "output": "Detected 1 distinct agent (assistant), domain = programming/documentation (Python). Main goal = single verification/explanation workflow (educational Q&A) — SIMPLE single-agent scenario."
+    },
+    {
+      "explanation": "2) ENTITY EXTRACTION: Extract Agents, Tasks, Tools, Inputs/Outputs and Human actors from observations following naming and priority rules.",
+      "output": "Extracted 1 Agent (Python Programming Assistant), 1 Task (Explain list comprehensions and provide examples), 1 Tool (Document Retrieval / RAG knowledge search), 1 Input (User Query), 1 Output (Explanation + Examples), 1 Human (End User)."
+    },
+    {
+      "explanation": "3) WORKFLOW CLASSIFICATION & TASK GENERATION: Apply SIMPLE workflow pattern (single agent -> single task).",
+      "output": "One consolidated task generated and assigned to the single agent. 1:1 mapping preserved (Agent -> Task)."
+    },
+    {
+      "explanation": "4) RELATION MAPPING: Map PERFORMS, CONSUMED_BY, PRODUCES, DELIVERS_TO and USES relations. Ensure Input→Agent→Task→Output→Human flow and tool connections.",
+      "output": "Created relations: Input CONSUMED_BY Agent; Agent PERFORMS Task; Task PRODUCES Output; Output DELIVERS_TO Human; Agent USES Tool."
+    },
+    {
+      "explanation": "5) QUALITY CHECK: Verify id consistency, include failures and optimizations.",
+      "output": "All relations reference existing entities. Added two identified failure modes (retrieval risk and single-agent risk) and two optimizations (tool enhancement and prompt refinement)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Python Documentation Assistant (RAG-enabled)",
+    "system_summary": "A single-agent RAG-powered Python documentation assistant that accepts beginner queries about Python syntax, performs document retrieval, and returns concise explanations and code examples. Workflow is a simple single-agent verification/explanation flow using a document retrieval tool.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Python Programming Assistant",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 7,
+            "line_end": 9
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Explain Python list comprehensions and provide practical examples",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 7,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Document Retrieval / RAG Knowledge Search",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Query: explanation request for Python list comprehensions",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Concise explanation and practical code examples of list comprehensions",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 7,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "End User (beginner learner)",
+        "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": 3
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 7,
+            "line_end": 9
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 7,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 10,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 6
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Document retrieval may return incomplete or low-relevance documents causing incorrect or unverified explanations.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 4,
+            "line_end": 6
+          }
+        ],
+        "affected_id": "tool_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "AGENT_ERROR",
+        "description": "Single-agent architecture creates a single point of failure (no independent verifier); misinterpretation of docs can propagate to final answer.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 7,
+            "line_end": 9
+          }
+        ],
+        "affected_id": "agent_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add caching, relevance re-ranking, and citation extraction to the document retrieval component to reduce latency and improve answer verifiability.",
+        "affected_ids": [
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 4,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine the assistant system prompt to require explicit citation of source documents and an optional short confidence score; consider a lightweight verification pass for critical claims.",
+        "affected_ids": [
+          "agent_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 7,
+            "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   ..."
+}

extraction_analysis/cot_extraction_20250907_201035_b2c14c48.json

@@ -0,0 +1,344 @@
+{
+  "timestamp": "20250907_201035",
+  "extraction_id": "b2c14c48",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) Analyze JSON structure: identify agents listed in data.agents and distinct agent-like entries in observations. Determine domain keywords (cost, savings, ticket, verification) => VERIFICATION workflow. Count distinct non-tool agents: ArithmeticProgressions_Expert, ProblemSolving_Expert, Verification_Expert (Computer_terminal is a tool).",
+      "output": "Detected 3 expert agents (HIGH priority) and 1 tool (Computer_terminal). Workflow classified as VERIFICATION (multi-agent, multi-step)."
+    },
+    {
+      "explanation": "2) Decide task decomposition per rules: For multi-agent verification workflows use 3 specialized tasks (one per expert). Ensure 1:1 mapping between agents and tasks and create a sequential flow: planning/orchestration -> price verification -> savings calculation -> produce output.",
+      "output": "Created 3 tasks: Plan Extraction & Orchestration, Price Verification for 2024, Savings Calculation. Assigned exactly one agent per task."
+    },
+    {
+      "explanation": "3) Map relations (CONSUMED_BY, PERFORMS, NEXT, PRODUCES, DELIVERS_TO, USES). Map evidence locations from observations and metadata for interaction_prompt_ref / raw_prompt_ref. Identify failure from metadata: Verification_Expert failed to collect price data for 2024.",
+      "output": "Relations and failure/optimization recommendations generated, with references to observation indices where behavior is visible."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Season-Pass Savings Verification System",
+    "system_summary": "Multi-agent verification workflow to confirm 2024 ticket and season-pass prices and compute savings for planned visits. Three expert agents coordinate: problem-solving/planning, price verification, and arithmetic calculation, supported by a Computer terminal tool.",
+    "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": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 6,
+            "line_end": 7
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "ArithmeticProgressions_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Plan Extraction and Orchestration",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Price Verification for 2024",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 0,
+            "line_end": 0
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Savings Calculation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Season Pass Savings Query",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified Costs and Savings Report",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "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_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          },
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Verification_Expert failed to collect authoritative 2024 price data for daily tickets and season passes (data retrieval omission).",
+        "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": 0,
+            "line_end": 0
+          }
+        ],
+        "affected_id": "agent_002"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enable or explicitly permit the Computer_terminal to fetch authoritative pricing (web/API access) or attach a cached price-data source. This reduces execution errors where Verification_Expert cannot collect live price data.",
+        "affected_ids": [
+          "tool_001",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Clarify and require an explicit verification step in the plan that includes sourcing and citing the authoritative price source (URL or dataset) so Verification_Expert must provide evidence for confirmed costs.",
+        "affected_ids": [
+          "task_002",
+          "agent_002"
+        ],
+        "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..."
+}