add

agentgraph/methods/production/openai_structured_extractor.py

@@ -169,20 +169,30 @@ ANALYSIS STEPS:
      * Contains "location", "restaurant", "proximity", "search" → DISCOVERY (3 tasks)  
      * Contains "probability", "game theory", "chemistry" → INTERDISCIPLINARY (3 tasks)
    - GENERATE tasks accordingly:
-     * VERIFICATION: 1 unified task, all agents PERFORM it
-     * DISCOVERY: 3 sequential tasks with NEXT relations
-     * INTERDISCIPLINARY: 3 domain tasks with NEXT relations
-
-4. RELATION MAPPING:
-   - PERFORMS: Match task count (1 or 3 agents→tasks)
-   - NEXT: Use only for 3-task workflows (2 NEXT relations)
-   - CONSUMED_BY/PRODUCES/DELIVERS_TO: Standard workflow flow
-   - USES/REQUIRED_BY: Essential tool connections only
-
-5. QUALITY CHECK:
+     * 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)
+   
+   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)
+
+5. QUALITY CHECK (Contextual Graph Enhanced):
    - Verify all relation IDs reference existing entities
    - Ensure complete workflow: Input→Agent→Task→Output→Human
    - Include 1-2 failures and optimizations
+   - CONTEXT VALIDATION: Ensure relationships align with trace context and domain semantics
+   - EDGE CASE HANDLING: Apply simplification for simple workflows, complexity for multi-domain scenarios
 
 FORMATTING:
 - IDs: agent_001, task_001, tool_001, etc.

extraction_analysis/cot_extraction_20250907_192829_3dadb467.json

@@ -0,0 +1,326 @@
+{
+  "timestamp": "20250907_192829",
+  "extraction_id": "3dadb467",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "JSON structure analysis: count distinct agents and determine domain and main goal.",
+      "output": "Distinct agent entries in data: 4 (ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert). Domain: cost verification / arithmetic savings calculation. Main goal: Verify prices (daily ticket and season pass for 2024) and compute savings — a SIMPLE VERIFICATION workflow (single verification task)."
+    },
+    {
+      "explanation": "Entity extraction according to extraction principles (classify Computer_terminal as a Tool, identify *_Expert agents).",
+      "output": "Agents: ArithmeticProgressions_Expert, ProblemSolving_Expert, Verification_Expert. Tool: Computer_terminal. Input: user question about season-pass savings. Output: Verified costs and computed savings. Human: End user receiving results."
+    },
+    {
+      "explanation": "Workflow mapping and relation decisions.",
+      "output": "Single high-level task 'Cost Verification and Savings Calculation' performed collaboratively by the three experts. Tool used by agents for data/reference checking. Standard flow: Input -> Agents -> Task -> Output -> Human. Include documented failure where Verification_Expert failed to collect price data; include optimization to enable reliable price retrieval."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Season Pass Savings Verification System",
+    "system_summary": "A small multi-agent verification workflow to confirm ticket and season-pass costs for California's Great America (summer 2024) and compute savings for a four-visit plan. Three domain experts collaborate on a single verification task while a computer terminal tool mediates data access.",
+    "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 Verification and Savings Calculation",
+        "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, 4 visits)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified Costs and Computed Savings",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "End User / Question Asker",
+        "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_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "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_005",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "agent_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "agent_003",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      }
+    ],
+    "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": "Provide the Verification_Expert (agent_003) secure access to a reliable pricing retrieval tool or enable Computer_terminal to fetch authoritative price data (official park website or archived pricing). This reduces execution errors due to lack of external data access.",
+        "affected_ids": [
+          "agent_003",
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine the Verification_Expert prompt to require explicit citation of data sources (URL or archival reference) when asserting verified prices, and include a fallback computation policy if live data cannot be retrieved.",
+        "affected_ids": [
+          "agent_003",
+          "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..."
+}

extraction_analysis/cot_extraction_20250907_192944_58a31c56.json

@@ -0,0 +1,267 @@
+{
+  "timestamp": "20250907_192944",
+  "extraction_id": "58a31c56",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) Count distinct agents and inspect observations: observations show a single assistant handling user query, knowledge search, inference, and follow-up. trace_metadata.component_stats.agent_count = 1 supports a single agent.",
+      "output": "Distinct agents = 1 (Python programming assistant)"
+    },
+    {
+      "explanation": "2) Identify domain and main goal: Trace is a RAG-powered documentation assistant answering a beginner user's question about Python list comprehensions and providing examples. This is a single end-to-end documentation/help task (multi-step internally: retrieval -> synthesis -> examples) but performed by one agent.",
+      "output": "Domain = programming documentation / RAG assistant. Main goal = answer user's Python list-comprehension question with explanation and examples."
+    },
+    {
+      "explanation": "3) Decide task structure using gold-standard patterns: With a single agent the workflow pattern is SIMPLE (1-2 agents => consolidated single task). Therefore represent the multi-step internal operations as one high-level Task 'Documentation-Assisted Answer Generation' performed by the single agent and using two tools (knowledge retriever and LLM).",
+      "output": "Task structure = 1 consolidated Task (Documentation-Assisted Answer Generation)"
+    },
+    {
+      "explanation": "4) Extract entities (Agents, Tools, Task, Input, Output, Human) and map relations (CONSUMED_BY, PERFORMS, USES, PRODUCES, DELIVERS_TO). Also identify failures and optimizations from trace metadata (timing/optimization tags). Raw prompt and interaction_prompt fields left empty per instructions; references to trace locations included.",
+      "output": "Entities and relations identified; 2 failures and 2 optimization recommendations prepared."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "RAG-Powered Python Documentation Assistant",
+    "system_summary": "A single-agent RAG (retrieval-augmented generation) assistant that consumes a beginner user's question, searches documentation, synthesizes an explanation, and returns examples. The agent uses a documentation knowledge base retriever and an LLM model to produce the final answer.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Python Programming Assistant",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 32,
+            "line_end": 40
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Documentation Knowledge Base / Retriever",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 30
+          }
+        ]
+      },
+      {
+        "id": "tool_002",
+        "type": "Tool",
+        "name": "LLM Model (gpt-4o-2024-11-20)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 36,
+            "line_end": 60
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Documentation-Assisted Answer Generation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 10,
+            "line_end": 90
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Question: 'What are Python list comprehensions and when should I use them?'",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 10,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Concise Explanation and Code Examples for List Comprehensions",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 32,
+            "line_end": 90
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Learner / End User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 10,
+            "line_end": 12
+          }
+        ]
+      }
+    ],
+    "relations": [
+      {
+        "id": "rel_001",
+        "source": "input_001",
+        "target": "agent_001",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 10,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 32,
+            "line_end": 90
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 30
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "task_001",
+        "target": "tool_002",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 36,
+            "line_end": 60
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 32,
+            "line_end": 90
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 62,
+            "line_end": 90
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Retriever may omit relevant documentation or return incomplete coverage, risking omission in the synthesized answer.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 20,
+            "line_end": 30
+          }
+        ],
+        "affected_id": "tool_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "High LLM latency and limited throughput (avg_llm_latency_ms / throughput metrics) could impair interactive responsiveness for learners.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 100,
+            "line_end": 120
+          }
+        ],
+        "affected_id": "agent_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Improve retriever recall by expanding query formulation, increasing index coverage, or adding document expansion / re-ranking to ensure more comprehensive retrieval for instructional queries.",
+        "affected_ids": [
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 20,
+            "line_end": 30
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Reduce perceived latency by caching frequent documentation responses, pre-warming the LLM for common beginner queries, or using a smaller local model for short explanatory replies.",
+        "affected_ids": [
+          "agent_001",
+          "tool_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 100,
+            "line_end": 120
+          }
+        ]
+      }
+    ]
+  },
+  "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_193154_ad2bf18b.json

@@ -0,0 +1,294 @@
+{
+  "timestamp": "20250907_193154",
+  "extraction_id": "ad2bf18b",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Count distinct agents and inspect trace domain.",
+      "output": "Detected 4 distinct agents (ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert). Domain: cost verification and simple savings calculation for season pass vs daily tickets (verification/calc)."
+    },
+    {
+      "explanation": "Decide workflow type according to gold-standard patterns.",
+      "output": "This matches SIMPLE VERIFICATION (costs/calculations) → use 1 consolidated task with a single lead PERFORMS relation and supporting agents INTERVENES/USES."
+    },
+    {
+      "explanation": "Extract entities: agents, tool, input, output, human, and the single task.",
+      "output": "Entities: Verification_Expert (lead agent), ProblemSolving_Expert (support), ArithmeticProgressions_Expert (support/calculator), Computer_terminal (Tool), Input (user question), Task (Season pass savings verification), Output (verified costs and savings), Human (End User)."
+    },
+    {
+      "explanation": "Map relations following verification workflow rules.",
+      "output": "Input is CONSUMED_BY Verification_Expert; Verification_Expert PERFORMS task_001; ProblemSolving_Expert and ArithmeticProgressions_Expert INTERVENE/ASSIST the task; Verification_Expert USES Computer_terminal; task PRODUCES output delivered to the End User."
+    },
+    {
+      "explanation": "Identify failures and optimizations from trace metadata.",
+      "output": "Failure: Verification_Expert failed to collect authoritative price data (metadata indicates mistake_agent Verification_Expert, mistake_reason). Optimizations: enable reliable price data retrieval (tool enhancement) and refine verification prompt/checklist (prompt refinement)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Season-Pass Savings Verification System",
+    "system_summary": "A small multi-agent verification workflow to confirm 2024 ticket and season-pass prices for California's Great America and compute savings for a 4-visit plan. One Verification Expert leads the verification task, supported by problem-solving and arithmetic experts and using a computer terminal tool.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "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_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": "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": 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": "Season pass savings query (user question)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified costs and computed savings",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "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": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "INTERVENES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "INTERVENES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Verification_Expert failed to collect authoritative 2024 price data for daily tickets and season passes.",
+        "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": 1,
+            "line_end": 1
+          }
+        ],
+        "affected_id": "agent_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Give the system access to a verified pricing data source (or enable the Computer_terminal tool to fetch authoritative 2024 pricing) and add a forced retrieval step for Verification_Expert before concluding verification.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine the Verification_Expert prompt to include an explicit checklist: (1) retrieve official daily ticket price, (2) retrieve official season pass price, (3) cite sources. This reduces hallucination and retrieval omission.",
+        "affected_ids": [
+          "agent_001",
+          "agent_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 9127,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_0.json\",\n  \"title\": \"Algorithm Sample 0: How much did I save by purchasing a season pass in...\",\n  \"description\": \"Multi-agent collaboration sample from algorithm-generated dataset. Agents: ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert. Question: How much did I save by purchasing a season pass instead of daily tickets for California's Great Amer...\",\n  \"trace_type\": \"multi_agent_collaboration\",\n  \"trace_source\": \"al..."
+}

extraction_analysis/cot_extraction_20250907_193302_b2970f5c.json

@@ -0,0 +1,295 @@
+{
+  "timestamp": "20250907_193302",
+  "extraction_id": "b2970f5c",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "JSON structure analysis: count distinct agents and identify workflow type.",
+      "output": "observations contain 4 components (user_query, knowledge_search, inference, follow_up). component_stats indicates 1 agent. Domain: programming documentation / RAG-powered assistant. Main goal: answer a user question about Python list comprehensions and provide examples → SIMPLE DOCUMENTATION/QA workflow (single consolidated task)."
+    },
+    {
+      "explanation": "Entity extraction: identify agents, tools, inputs/outputs, and the single task based on Gold standard patterns.",
+      "output": "Agents: 1 (Python Documentation Assistant). Tools: Documentation Search API, Knowledge Base (documents), LLM model. Task: one consolidated task 'Explain Python list comprehensions and provide examples'. Input: user query. Output: explanation + code examples. Human: end user/learner."
+    },
+    {
+      "explanation": "Relation mapping according to workflow type rules (verification/simple QA = 1 PERFORMS relation).",
+      "output": "Map Input → Agent (CONSUMED_BY), Agent → Task (PERFORMS), Task → Output (PRODUCES), Output → Human (DELIVERS_TO). Agent USES tools (Documentation Search, Knowledge Base, LLM)."
+    },
+    {
+      "explanation": "Quality checks and risk identification: ensure entity IDs referenced by relations exist and include failures/optimizations.",
+      "output": "All relations reference existing entity ids. Two risk items added: an unsupported empirical claim ('20-30% faster') flagged as RETRIEVAL_ERROR; potential hallucination risk flagged for generated claims. Two optimizations recommended: citation/prompt refinement and tool enhancement for evidence linking."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Python Documentation Assistant (RAG-powered)",
+    "system_summary": "A single-agent RAG-enabled documentation assistant that consumes a user's Python question, searches documentation, and generates an explanation with code examples. Workflow is a simple documentation/QA pipeline: user query → assistant uses search + LLM → produces explanation and examples → delivers to user.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Python Documentation Assistant",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 15,
+            "line_end": 16
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Documentation Search API",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 7,
+            "line_end": 14
+          }
+        ]
+      },
+      {
+        "id": "tool_002",
+        "type": "Tool",
+        "name": "LLM Model (gpt-4o-2024-11-20)",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 21
+          }
+        ]
+      },
+      {
+        "id": "tool_003",
+        "type": "Tool",
+        "name": "Knowledge Base / Documentation Corpus",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 7,
+            "line_end": 14
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Explain Python list comprehensions and provide practical examples",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 15,
+            "line_end": 35
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Python list-comprehension query",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Explanation and code examples for list comprehensions",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 15,
+            "line_end": 45
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "End User / Learner",
+        "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": 1,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 15,
+            "line_end": 30
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 15,
+            "line_end": 45
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 31,
+            "line_end": 45
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 7,
+            "line_end": 14
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "agent_001",
+        "target": "tool_002",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 21
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "agent_001",
+        "target": "tool_003",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 7,
+            "line_end": 14
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Empirical claim ('List comprehensions are typically 20-30% faster than equivalent for loops') lacks explicit citation to a supporting benchmark in retrieved documents.",
+        "raw_text": "List comprehensions are not only more concise but also typically 20-30% faster than equivalent for loops!",
+        "raw_text_ref": [
+          {
+            "line_start": 31,
+            "line_end": 35
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "HALLUCINATION",
+        "description": "Possibility of unsupported or overgeneralized statements if the assistant extrapolates beyond provided documents.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": 15,
+            "line_end": 30
+          }
+        ],
+        "affected_id": "tool_002"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Require the assistant to attach inline citations for empirical/performance claims and add a short 'Sources' section linking retrieved documents when presenting benchmarks or percentages.",
+        "affected_ids": [
+          "agent_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 31,
+            "line_end": 35
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Enhance the Documentation Search API / Knowledge Base with versioned documents and automated citation linking so the assistant can return provenance with each factual claim.",
+        "affected_ids": [
+          "tool_001",
+          "tool_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 7,
+            "line_end": 14
+          }
+        ]
+      }
+    ]
+  },
+  "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_193444_7e2a726f.json

@@ -0,0 +1,300 @@
+{
+  "timestamp": "20250907_193444",
+  "extraction_id": "7e2a726f",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "JSON structure analysis: counted distinct agents in data.agents (3 expert agents + 1 Computer terminal tool). Determined domain as a cost verification task (contains 'cost', 'savings', 'verification'), mapping to the SIMPLE VERIFICATION workflow pattern (1 consolidated task).",
+      "output": "Workflow classified as VERIFICATION (single consolidated task). Agents identified: ArithmeticProgressions_Expert, ProblemSolving_Expert, Verification_Expert. Computer_terminal classified as Tool."
+    },
+    {
+      "explanation": "Entity extraction: created Agent entities for the three experts, a Tool entity for Computer_terminal, a single Task entity for cost verification and savings calculation, Input/Output endpoints, and a Human recipient. Mapped agent roles from observations and metadata. Used Verification_Expert as the lead performing agent (metadata indicates Verification role and mistake_agent).",
+      "output": "Entities and roles prepared. One consolidated task assigned to Verification_Expert with other experts as intervening collaborators. Computer_terminal marked as a supporting tool used by agents."
+    },
+    {
+      "explanation": "Relation mapping: applied VERIFICATION pattern rules — single PERFORMS relation from the lead Verification_Expert to the task; other experts INTERVENE. Input consumed by lead agent; task PRODUCES output delivered to human. Tools are connected via USES relations. Added failure and optimization entries based on trace metadata.",
+      "output": "Relations and quality items created. All relation ids reference existing entities. Included one execution failure (Verification_Expert failed to collect price data) and two optimizations."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Season-Pass Cost Verification System",
+    "system_summary": "A small multi-agent verification workflow to confirm ticket and season-pass prices and compute savings for a specified set of visits. Three expert agents collaborate (verification lead, problem solving, arithmetic), with a computer terminal tool supporting the interaction. The workflow is a single consolidated verification task producing a verified cost and savings output for an end user.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "ArithmeticProgressions_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "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": 6,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Season-Pass Cost Verification & Savings Calculation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User 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 Statement",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "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_003",
+        "type": "CONSUMED_BY",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "INTERVENES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "INTERVENES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 5
+          }
+        ]
+      },
+      {
+        "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": 3
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "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
+          }
+        ]
+      }
+    ],
+    "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, causing an unverifiable assumption.",
+        "raw_text": "",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "agent_003"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Provide the Verification_Expert with an explicit data-retrieval tool or API integration (price-data scraper / official park pricing API) so price facts can be fetched reliably instead of assumed.",
+        "affected_ids": [
+          "agent_003",
+          "tool_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Clarify the verification plan to require explicit citation of source URLs or data snapshots for ticket/season-pass prices and add a short data-collection checklist to avoid assumptions.",
+        "affected_ids": [
+          "task_001",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 9127,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_0.json\",\n  \"title\": \"Algorithm Sample 0: How much did I save by purchasing a season pass in...\",\n  \"description\": \"Multi-agent collaboration sample from algorithm-generated dataset. Agents: ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert. Question: How much did I save by purchasing a season pass instead of daily tickets for California's Great Amer...\",\n  \"trace_type\": \"multi_agent_collaboration\",\n  \"trace_source\": \"al..."
+}

extraction_analysis/cot_extraction_20250907_193551_4eb59423.json

@@ -0,0 +1,376 @@
+{
+  "timestamp": "20250907_193551",
+  "extraction_id": "4eb59423",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Analyze JSON trace to count agents, identify domain and main goal, and choose workflow pattern.",
+      "output": "Detected 4 distinct components in agents/observations: 3 specialist agents (Location-Based_Services_Expert, Eateries_Expert, DataVerification_Expert) and 1 Computer_terminal (tool). Domain: location_based_services. Main goal: find the closest eatery to Harkness Memorial State Park open at 11pm on Wednesdays → maps/time-based discovery task (Complex Sequential Workflow)."
+    },
+    {
+      "explanation": "Extract entities (Agents, Tasks, Tools, Input/Output, Human) following Gold-standard patterns for discovery workflows and map relations.",
+      "output": "Mapped 4 entities as agents/tools, defined 3 sequential high-level tasks (Geographic Proximity Analysis → Restaurant Data Collection → Operating Hours Validation), defined input/output and human consumer, and mapped PERFORMS, NEXT, CONSUMED_BY, PRODUCES, DELIVERS_TO, and USES relations. Identified execution failure in DataVerification_Expert and a retrieval/planning failure where no eateries met the criteria."
+    },
+    {
+      "explanation": "Quality checks: ensure all relations reference existing entities, include 1-2 failures and optimization recommendations.",
+      "output": "All relation IDs reference defined entities. Added two failures (execution error and retrieval error) and two targeted optimizations (tool robustness + prompt/workflow improvements)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Location-Based Restaurant Discovery System",
+    "system_summary": "Multi-agent location-based discovery pipeline that locates the park, collects candidate eateries, verifies operating hours, and returns the closest eatery open at 11pm on Wednesdays. The system uses a Computer_terminal tool for web/search actions and coordinates three specialist agents in a sequential workflow.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Location-Based Services Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Eateries Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "DataVerification Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer Terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "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 (name, address, distance, 11pm Wed confirmation)",
+        "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 raised an exception while executing verification code (TypeError: 'NoneType' object is not iterable).",
+        "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": "Expanded search and manual verification failed to find any eateries open at 11 PM on Wednesdays near the park (no candidate met criteria).",
+        "raw_text": "None of the eateries identified near Harkness Memorial State Park meet the requirement of being open until 11 PM on Wednesdays.",
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ],
+        "affected_id": "task_003"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Harden the Computer Terminal / perform_web_search wrapper to return empty lists rather than None and add retries/timeouts. This reduces execution failures (e.g., TypeError) and improves robustness for all agents that call the search tool.",
+        "affected_ids": [
+          "tool_001",
+          "agent_003",
+          "agent_002",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine the search & verification workflow to include fallback strategies: expand radius automatically, query additional data sources (Google Places, official restaurant pages), and allow the DataVerification agent to record confidence levels and partial matches. This increases chance of retrieving late-night options and surfaces why no result met criteria.",
+        "affected_ids": [
+          "agent_001",
+          "agent_002",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": null,
+            "line_end": null
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 29909,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_1.json\",\n  \"title\": \"Algorithm Sample 1: What is the closest eatery to Harkness Memorial State Park t...\",\n  \"description\": \"Complex location-based services sample with 4 specialized agents. Involves geographic queries, time-based filtering, and data verification.\",\n  \"trace_type\": \"location_based_services\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"location_services\",\n    \"data_verification\",\n    \"re..."
+}

extraction_analysis/cot_extraction_20250907_193644_720b404a.json

@@ -0,0 +1,345 @@
+{
+  "timestamp": "20250907_193644",
+  "extraction_id": "720b404a",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "Count agents and determine workflow type from trace metadata and content.",
+      "output": "Detected 4 distinct agents in observations: Probability_Expert, TheoreticalChemistry_Expert, Verification_Expert, Computer_terminal (Computer_* classified as Tool). The trace describes a cross-disciplinary simulation (probability + theoretical chemistry) to answer a riddle — this maps to an INTERDISCIPLINARY_ANALYSIS pattern (3 domain-specific tasks)."
+    },
+    {
+      "explanation": "Extract entities following Gold-standard mapping for interdisciplinary workflows and map relations according to execution evidence (simulation run, verification dialogue, metadata error).",
+      "output": "Generate 3 tasks (Statistical Analysis, Chemical Modeling, Solution Verification), 3 PERFORMS relations (1:1 agent→task), NEXT links chaining tasks, Input→Agent consumption, Tool usage by Probability_Expert, and standard PRODUCES/DELIVERS_TO flow. Identify failures from metadata and verification dialogue and propose optimizations."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Cross-Disciplinary Ping-Pong Simulation and Verification System",
+    "system_summary": "A multi-agent system combining probability simulation and theoretical-chemistry review to solve a game-show riddle. The Probability_Expert implements large-scale simulation (using Computer_terminal) to estimate ejection frequencies; TheoreticalChemistry_Expert provides domain modeling insight; Verification_Expert confirms results and produces the final recommendation delivered to the contestant.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Probability_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 100,
+            "line_end": 160
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "TheoreticalChemistry_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 60,
+            "line_end": 110
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 170,
+            "line_end": 200
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 160,
+            "line_end": 176
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Statistical Simulation and Frequency Analysis",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 100,
+            "line_end": 160
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Domain Modeling (Mechanics/Chemical Dynamics Insight)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 60,
+            "line_end": 110
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Result Verification and Recommendation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 170,
+            "line_end": 200
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Riddle: 'Pick That Ping-Pong' Problem Statement",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 40
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Recommended Ball Choice (simulation result)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 170,
+            "line_end": 176
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Contestant / 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": 40
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 100,
+            "line_end": 160
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 60,
+            "line_end": 110
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 170,
+            "line_end": 200
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 100,
+            "line_end": 160
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 60,
+            "line_end": 200
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 170,
+            "line_end": 176
+          }
+        ]
+      },
+      {
+        "id": "rel_008",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 170,
+            "line_end": 176
+          }
+        ]
+      },
+      {
+        "id": "rel_009",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 160,
+            "line_end": 176
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "Probability_Expert made an error in the simulation implementation resulting in an incorrect outcome (simulation returned ball 2 while ground truth indicates 3).",
+        "raw_text": "The agent made an error in the simulation implementation, resulting in an incorrect outcome.",
+        "raw_text_ref": [
+          {
+            "line_start": 10,
+            "line_end": 16
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "AGENT_ERROR",
+        "description": "Verification_Expert and TheoreticalChemistry_Expert confirmed the simulation result without detecting the implementation error, causing acceptance of an incorrect recommendation.",
+        "raw_text": "Based on the implementation and results, I agree that picking ball 2 is optimal... Yes, I agree with the conclusion based on the steps provided and the simulation results.",
+        "raw_text_ref": [
+          {
+            "line_start": 170,
+            "line_end": 200
+          }
+        ],
+        "affected_id": "agent_003"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Introduce an independent analytical check (closed-form reasoning or small-scale deterministic simulation) and unit tests to validate the simulator implementation before relying on stochastic runs. Require the Verification_Expert to inspect core simulation logic and key edge cases.",
+        "affected_ids": [
+          "agent_001",
+          "agent_003"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 100,
+            "line_end": 160
+          },
+          {
+            "line_start": 170,
+            "line_end": 200
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Log deterministic seeds and execution traces for Computer_terminal runs and add code-review and reproducibility checkpoints (e.g., small-run exact enumerations) to detect implementation mistakes early.",
+        "affected_ids": [
+          "tool_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 160,
+            "line_end": 176
+          }
+        ]
+      }
+    ]
+  },
+  "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_193759_b8b8652c.json

@@ -0,0 +1,299 @@
+{
+  "timestamp": "20250907_193759",
+  "extraction_id": "b8b8652c",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count distinct agents, identify domain and main goal.",
+      "output": "Detected 4 distinct agents (Literary_Analysis_Expert, NorseMythology_Expert, Verification_Expert, Computer_terminal). Domain: academic literature analysis. Main goal: verification of a quoted word in Emily Midkiff's June 2014 article (a single verification task)."
+    },
+    {
+      "explanation": "2) Entity extraction using *_Expert patterns, identify tools, inputs/outputs, and human stakeholder.",
+      "output": "Extracted 4 agents (3 Experts + 1 Computer tool), 1 consolidated verification task, 1 input, 1 output, and 1 human stakeholder."
+    },
+    {
+      "explanation": "3) Workflow classification & task generation following the GOLD pattern rules.",
+      "output": "Classified as SIMPLE VERIFICATION workflow → produce 1 Task performed by a single lead Verification_Expert; Literary_Analysis_Expert and NorseMythology_Expert act as supporting/intervening agents; Computer_terminal is a tool used."
+    },
+    {
+      "explanation": "4) Relation mapping: assign PERFORMS, INTERVENES, USES, CONSUMED_BY, PRODUCES, DELIVERS_TO consistent with trace.",
+      "output": "Mapped Input→Verification_Expert (CONSUMED_BY), Verification_Expert PERFORMS task, two experts INTERVENE on the task, Verification_Expert USES Computer_terminal, task PRODUCES an Output delivered to the Human."
+    },
+    {
+      "explanation": "5) Quality checks and risk identification.",
+      "output": "Verified all relation targets reference existing entities. Identified two failures from trace metadata and observations (incorrect tool selection by Literary_Analysis_Expert and arXiv search failure). Proposed two optimizations (use appropriate scholarly DBs and require source-confirmation step)."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Scholarly Literature Verification System (Fafnir Article Check)",
+    "system_summary": "Multi-agent academic analysis system to verify a quoted word in a specific scholarly article. A single consolidated verification task is performed by a Verification Expert, supported by a Literary Analysis Expert and a Norse Mythology Expert; a Computer terminal tool is used for searches and retrieval.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Verification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 200,
+            "line_end": 220
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "Literary_Analysis_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 40,
+            "line_end": 90
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "NorseMythology_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 25
+          }
+        ]
+      },
+      {
+        "id": "agent_004",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 60,
+            "line_end": 120
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Verify Quoted Word in Emily Midkiff's June 2014 'Fafnir' Article",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Research Query: identify quoted word from two authors in Midkiff (June 2014, Fafnir)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified Word (quoted by two different authors)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 28
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Requesting Researcher / 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": 1,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 200,
+            "line_end": 220
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_001",
+        "type": "INTERVENES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 40,
+            "line_end": 90
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_001",
+        "type": "INTERVENES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 25
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "agent_001",
+        "target": "agent_004",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 60,
+            "line_end": 120
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 28
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 20,
+            "line_end": 28
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Literary_Analysis_Expert invoked arxiv_search which is inappropriate for locating an article in Fafnir; led to irrelevant arXiv results.",
+        "raw_text": "The code calls the arxiv_search tool; however, using other tools, such as perform_web_search, would be more appropriate because arXiv is primarily academically oriented.",
+        "raw_text_ref": [
+          {
+            "line_start": 12,
+            "line_end": 18
+          }
+        ],
+        "affected_id": "agent_002"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "Computer_terminal's arxiv_search returned unrelated entries and did not locate the target Fafnir article.",
+        "raw_text": "arXiv search results returned items unrelated to Emily Midkiff's Fafnir article (FAFNIR entries about fusion/neutron sources and other 2014 items).",
+        "raw_text_ref": [
+          {
+            "line_start": 60,
+            "line_end": 120
+          }
+        ],
+        "affected_id": "agent_004"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Prefer targeted scholarly/journal databases (JSTOR, Project MUSE, publisher's site) or general web search functions for humanities journals like Fafnir instead of arXiv. Implement tool-selection rules for domain-appropriate sources.",
+        "affected_ids": [
+          "agent_002",
+          "agent_004"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 12,
+            "line_end": 18
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Add an explicit verification substep requiring confirmation of primary-source access (e.g., link or page snapshot) before finalizing the verified word to avoid relying on secondary or misdirected searches.",
+        "affected_ids": [
+          "task_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 200,
+            "line_end": 220
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 18452,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_14.json\",\n  \"title\": \"Algorithm Sample 14: In Emily Midkiff's June 2014 article in a journal named for ...\",\n  \"description\": \"Scholarly research system combining literary analysis and Norse mythology expertise for academic paper investigation\",\n  \"trace_type\": \"academic_literature_analysis\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"academic_research\",\n    \"literature_analysis\",\n    \"norse_mythology\"..."
+}

extraction_analysis/cot_extraction_20250907_193939_16ca33f3.json

@@ -0,0 +1,392 @@
+{
+  "timestamp": "20250907_193939",
+  "extraction_id": "16ca33f3",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: counted distinct agents in data.agents and inspected observations for execution evidence and errors. Determined domain is wildlife_data_analysis with main goal: count nonindigenous crocodiles found in Florida (2000–2020). Workflow is a multi-step data-processing and verification pipeline (complex sequential workflow).",
+      "output": "4 distinct components listed in data.agents (Computer_terminal, DataAnalysis_Expert, DataVerification_Expert, StatisticalAnalysis_Expert). Main goal: produce verified numeric count for 2000–2020."
+    },
+    {
+      "explanation": "2) Entity extraction: extracted 3 specialist agents (*_Expert pattern) as Agents, recognized Computer_terminal as a Tool (Computer* rule). Created 3 sequential high-level tasks matching the multi-step plan found in observations (acquisition → verification/cleaning → statistical counting). Defined a single Input (research question/manager task), one Output (numeric count), and a Human stakeholder.",
+      "output": "Entities prepared: 3 Agents, 1 Tool, 3 Tasks, 1 Input, 1 Output, 1 Human."
+    },
+    {
+      "explanation": "3) Workflow classification & relation mapping: mapped 1:1 PERFORMS relations between the 3 specialist agents and 3 tasks, added NEXT chain between tasks, mapped Input→Agent (CONSUMED_BY), task→output (PRODUCES), and output→human (DELIVERS_TO). Linked tool usage with USES relation from acquisition/verification tasks to Computer_terminal. Collected execution failure evidence and derived optimizations.",
+      "output": "Complete workflow graph created with required relation types and evidentiary refs."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Wildlife Statistical Analysis & Verification System (Nonindigenous Crocodile Count)",
+    "system_summary": "Multi-agent pipeline for extracting, verifying, and statistically analyzing USGS nonindigenous aquatic-species data to produce a verified count of nonindigenous crocodiles found in Florida from 2000–2020. The pipeline comprises dataset acquisition, verification/cleaning, and statistical counting performed by specialized experts with a computer terminal tool for execution.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "DataAnalysis_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 9,
+            "line_end": 9
+          }
+        ]
+      },
+      {
+        "id": "agent_002",
+        "type": "Agent",
+        "name": "DataVerification_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "agent_003",
+        "type": "Agent",
+        "name": "StatisticalAnalysis_Expert",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Computer_terminal",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 10,
+            "line_end": 10
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Dataset Acquisition (confirm URL and download)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          },
+          {
+            "line_start": 9,
+            "line_end": 9
+          }
+        ]
+      },
+      {
+        "id": "task_002",
+        "type": "Task",
+        "name": "Data Verification & Cleaning (format check, extract relevant records)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          },
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "task_003",
+        "type": "Task",
+        "name": "Statistical Analysis & Counting (filter 2000–2020, count crocodile records)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "Research Question: count nonindigenous crocodiles in Florida (2000–2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Verified count of nonindigenous crocodiles found in Florida (2000–2020)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 0,
+            "line_end": 0
+          },
+          {
+            "line_start": 12,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "Manager / End User",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "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": 9,
+            "line_end": 9
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 9,
+            "line_end": 9
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "agent_002",
+        "target": "task_002",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "agent_003",
+        "target": "task_003",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "task_001",
+        "target": "task_002",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "task_002",
+        "target": "task_003",
+        "type": "NEXT",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 6
+          }
+        ]
+      },
+      {
+        "id": "rel_007",
+        "source": "task_003",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          },
+          {
+            "line_start": 12,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "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": "task_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 10,
+            "line_end": 10
+          }
+        ]
+      },
+      {
+        "id": "rel_010",
+        "source": "task_002",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "RETRIEVAL_ERROR",
+        "description": "DataAnalysis_Expert failed to locate the correct URL for the USGS dataset, leading to an incorrect placeholder download.",
+        "raw_text": "The agent failed to locate the correct URL for the dataset from the USGS Nonindigenous Aquatic Species database.",
+        "raw_text_ref": [
+          {
+            "line_start": 12,
+            "line_end": 12
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "CSV parsing failed because the downloaded file was actually HTML (pandas ParserError), preventing data extraction.",
+        "raw_text": "pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 8, saw 2\n<!doctype html>\n<html>...",
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ],
+        "affected_id": "tool_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add URL/content validation before download (use HEAD requests, check Content-Type and simple content sniffing) and automatic retry with alternate endpoints. This will reduce HTML-placeholder downloads and parsing errors.",
+        "affected_ids": [
+          "agent_001",
+          "tool_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          },
+          {
+            "line_start": 12,
+            "line_end": 12
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine agent instructions so DataVerification_Expert runs a quick format/content check immediately after download (detect HTML vs CSV), and escalate to re-locating the canonical USGS API endpoint if mismatch detected.",
+        "affected_ids": [
+          "agent_002",
+          "task_002"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 5,
+            "line_end": 6
+          }
+        ]
+      }
+    ]
+  },
+  "input_trace_length": 13048,
+  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_16.json\",\n  \"title\": \"Algorithm Sample 16: How many nonindigenous crocodiles were found in Florida from...\",\n  \"description\": \"Statistical analysis system for ecological data processing, focusing on invasive species tracking and government dataset analysis\",\n  \"trace_type\": \"wildlife_data_analysis\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"data_analysis\",\n    \"wildlife_research\",\n    \"statistical_ana..."
+}

extraction_analysis/cot_extraction_20250907_194043_a660d64f.json

@@ -0,0 +1,268 @@
+{
+  "timestamp": "20250907_194043",
+  "extraction_id": "a660d64f",
+  "model": "gpt-5-mini",
+  "reasoning_steps": [
+    {
+      "explanation": "1) JSON structure analysis: count distinct agents and determine domain & main goal.",
+      "output": "Found 1 distinct agent (single assistant). Domain: programming documentation / RAG-powered documentation assistant. Main goal: answer a user's Python documentation question and provide examples (single documentation/QA workflow)."
+    },
+    {
+      "explanation": "2) Entity extraction according to Gold-standard patterns: identify Agents, Tasks, Tools, Inputs/Outputs, Humans.",
+      "output": "Mapped a single high-priority Agent (Python Documentation Assistant), one high-priority Task (Documentation Retrieval and Explanation), two supporting Tools (Documentation Knowledge Base / Search and Computer Terminal execution environment), Input (User Query), Output (Answer + Code Examples), and Human (End User Learner)."
+    },
+    {
+      "explanation": "3) Workflow classification & task generation: apply SIMPLE DOCUMENTATION/QA pattern.",
+      "output": "Classified as SIMPLE DOCUMENTATION/QA: a one-task workflow performed by the single agent. This yields one PERFORMS relation (agent -> task) and the standard Input -> Agent -> Task -> Output -> Human flow. Tools are linked via USES relations."
+    },
+    {
+      "explanation": "4) Relations & quality checks: create relations per KnowPrompt rules and sanity-check IDs.",
+      "output": "Generated relations: CONSUMED_BY (input->agent), PERFORMS (agent->task), PRODUCES (task->output), DELIVERS_TO (output->human), USES (agent->tools). Validated that all relation source/target IDs reference existing entities. Added two realistic failures and two optimizations based on trace performance and architecture."
+    }
+  ],
+  "knowledge_graph": {
+    "system_name": "Python Documentation Assistant (RAG-powered)",
+    "system_summary": "A single-agent RAG-enabled documentation assistant that searches a documentation knowledge base, composes concise explanations, and returns examples for Python syntax questions. Workflow is a simple documentation/QA loop: consume user query, retrieve docs, generate explanation and examples, and return results to the user.",
+    "entities": [
+      {
+        "id": "agent_001",
+        "type": "Agent",
+        "name": "Python Documentation Assistant",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "task_001",
+        "type": "Task",
+        "name": "Documentation Retrieval and Explanation",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "tool_001",
+        "type": "Tool",
+        "name": "Documentation Knowledge Base / Search API",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "tool_002",
+        "type": "Tool",
+        "name": "Computer Terminal / Execution Environment",
+        "importance": "MEDIUM",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "input_001",
+        "type": "Input",
+        "name": "User Python Documentation Query",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 1,
+            "line_end": 1
+          }
+        ]
+      },
+      {
+        "id": "output_001",
+        "type": "Output",
+        "name": "Explanation and Code Examples (List Comprehensions)",
+        "importance": "HIGH",
+        "raw_prompt": "",
+        "raw_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "human_001",
+        "type": "Human",
+        "name": "End User (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": 1
+          }
+        ]
+      },
+      {
+        "id": "rel_002",
+        "source": "agent_001",
+        "target": "task_001",
+        "type": "PERFORMS",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 3
+          }
+        ]
+      },
+      {
+        "id": "rel_003",
+        "source": "task_001",
+        "target": "output_001",
+        "type": "PRODUCES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 3,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_004",
+        "source": "output_001",
+        "target": "human_001",
+        "type": "DELIVERS_TO",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 4,
+            "line_end": 4
+          }
+        ]
+      },
+      {
+        "id": "rel_005",
+        "source": "agent_001",
+        "target": "tool_001",
+        "type": "USES",
+        "importance": "HIGH",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 2
+          }
+        ]
+      },
+      {
+        "id": "rel_006",
+        "source": "agent_001",
+        "target": "tool_002",
+        "type": "USES",
+        "importance": "MEDIUM",
+        "interaction_prompt": "",
+        "interaction_prompt_ref": [
+          {
+            "line_start": 2,
+            "line_end": 4
+          }
+        ]
+      }
+    ],
+    "failures": [
+      {
+        "id": "failure_001",
+        "risk_type": "EXECUTION_ERROR",
+        "description": "High LLM latency observed (avg_llm_latency_ms ~1837) which can slow interactivity and degrade user experience.",
+        "raw_text": "avg_llm_latency_ms: 1837 (timing analytics)",
+        "raw_text_ref": [
+          {
+            "line_start": 10,
+            "line_end": 10
+          }
+        ],
+        "affected_id": "agent_001"
+      },
+      {
+        "id": "failure_002",
+        "risk_type": "PLANNING_ERROR",
+        "description": "Single-agent architecture with no specialist sub-agents may limit handling of more complex multi-step or domain-specific queries.",
+        "raw_text": "component_hierarchy.agents: [\"\"] and agent_count: 1",
+        "raw_text_ref": [
+          {
+            "line_start": 11,
+            "line_end": 11
+          }
+        ],
+        "affected_id": "agent_001"
+      }
+    ],
+    "optimizations": [
+      {
+        "id": "opt_001",
+        "recommendation_type": "TOOL_ENHANCEMENT",
+        "description": "Add a caching layer to the Documentation Knowledge Base / Search API to reduce repeated retrieval latency for common queries (especially for high-frequency beginner questions).",
+        "affected_ids": [
+          "tool_001",
+          "agent_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 10,
+            "line_end": 10
+          }
+        ]
+      },
+      {
+        "id": "opt_002",
+        "recommendation_type": "PROMPT_REFINEMENT",
+        "description": "Refine and shorten the retrieval + system prompt pipeline (or introduce lightweight retrieval-only prefilters) to reduce token usage and LLM latency; consider introducing a small 'retrieval expert' sub-agent to handle search and ranking while the main assistant focuses on composition.",
+        "affected_ids": [
+          "agent_001",
+          "task_001"
+        ],
+        "raw_text_ref": [
+          {
+            "line_start": 2,
+            "line_end": 3
+          }
+        ]
+      }
+    ]
+  },
+  "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   ..."
+}