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{
"system_name": "Oxford Economics Inquiry and Response System",
"system_summary": "This system manages Oxford Economics inquiries through a streamlined workflow. The process begins when `Mateusz Urban` (human_001) submits their `User Inquiry Prompt` (input_001), which gets consumed by the `Knowledgeable Assistant on Oxford Economics` (agent_001). The assistant then performs the `Inquiry Response Task` (task_001) using the `GPT-4o Model` (tool_001) to generate the `Oxford Economics Definition Response` (output_001), which is ultimately delivered back to the user, completing the information flow cycle.",
"entities": [
{
"id": "agent_001",
"type": "Agent",
"name": "Knowledgeable Assistant on Oxford Economics",
"importance": "HIGH",
"raw_prompt": "You are a knowledgeable assistant on Oxford Economics designed to answer questions accurately based on the provided context. Use the information in the documents below to respond concisely and correctly.",
"raw_prompt_ref": [
{
"line_start": 31,
"line_end": 32,
"confidence": 0.95
}
]
},
{
"id": "task_001",
"type": "Task",
"name": "Inquiry Response Task",
"importance": "HIGH",
"raw_prompt": "Process user inquiry about Oxford Economics and generate an accurate, contextual response based on available information and company background.",
"raw_prompt_ref": [
{
"line_start": 26,
"line_end": 28,
"confidence": 0.9
}
]
},
{
"id": "output_001",
"type": "Output",
"name": "Oxford Economics Definition Response",
"importance": "HIGH",
"raw_prompt": "Oxford Economics provides economic analysis, forecasting, and consultancy services.",
"raw_prompt_ref": [
{
"line_start": 20,
"line_end": 20,
"confidence": 1.0
}
]
},
{
"id": "input_001",
"type": "Input",
"name": "User Inquiry Prompt",
"importance": "HIGH",
"raw_prompt": "what does oxford eonomics present?",
"raw_prompt_ref": [
{
"line_start": 19,
"line_end": 19,
"confidence": 1.0
}
]
},
{
"id": "human_001",
"type": "Human",
"name": "Mateusz Urban",
"importance": "MEDIUM",
"raw_prompt": "User interaction pattern: submits inquiry and receives response",
"raw_prompt_ref": [
{
"line_start": 31,
"line_end": 31,
"confidence": 0.8
}
]
},
{
"id": "tool_001",
"type": "Tool",
"name": "GPT-4o Model (2024-11-20)",
"importance": "HIGH",
"raw_prompt": "AI language model configured for Oxford Economics domain knowledge with structured response capabilities and context-aware processing.",
"raw_prompt_ref": [
{
"line_start": 49,
"line_end": 49,
"confidence": 1.0
}
]
}
],
"relations": [
{
"id": "relation_001",
"source": "input_001",
"target": "agent_001",
"type": "CONSUMED_BY",
"importance": "HIGH",
"interaction_prompt": "User inquiry processed by assistant: 'what does oxford eonomics present?'",
"interaction_prompt_ref": [
{
"line_start": 19,
"line_end": 19,
"confidence": 1.0
}
]
},
{
"id": "relation_002",
"source": "agent_001",
"target": "task_001",
"type": "PERFORMS",
"importance": "HIGH",
"interaction_prompt": "Assistant actively processes the Oxford Economics inquiry using domain knowledge",
"interaction_prompt_ref": [
{
"line_start": 31,
"line_end": 32,
"confidence": 0.9
}
]
},
{
"id": "relation_003",
"source": "task_001",
"target": "output_001",
"type": "PRODUCES",
"importance": "HIGH",
"interaction_prompt": "Task generates structured response about Oxford Economics services",
"interaction_prompt_ref": [
{
"line_start": 20,
"line_end": 20,
"confidence": 1.0
}
]
},
{
"id": "relation_004",
"source": "output_001",
"target": "human_001",
"type": "DELIVERS_TO",
"importance": "HIGH",
"interaction_prompt": "Response delivered to Mateusz Urban with Oxford Economics definition",
"interaction_prompt_ref": [
{
"line_start": 20,
"line_end": 20,
"confidence": 1.0
}
]
},
{
"id": "relation_005",
"source": "agent_001",
"target": "tool_001",
"type": "USES",
"importance": "HIGH",
"interaction_prompt": "Assistant leverages GPT-4o model capabilities for processing and response generation",
"interaction_prompt_ref": [
{
"line_start": 49,
"line_end": 49,
"confidence": 0.9
}
]
},
{
"id": "relation_006",
"source": "task_001",
"target": "tool_001",
"type": "REQUIRED_BY",
"importance": "HIGH",
"interaction_prompt": "Task execution requires GPT-4o model for natural language processing",
"interaction_prompt_ref": [
{
"line_start": 49,
"line_end": 49,
"confidence": 0.8
}
]
}
],
"failures": [
{
"id": "failure_001",
"risk_type": "HALLUCINATION",
"description": "User input contains spelling error 'eonomics' instead of 'economics' which may lead to misinterpretation or processing errors.",
"raw_text": "what does oxford eonomics present?",
"raw_text_ref": [
{
"line_start": 19,
"line_end": 19,
"confidence": 1.0
}
],
"affected_id": "input_001"
},
{
"id": "failure_002",
"risk_type": "AGENT_ERROR",
"description": "System prompt contains spelling error 'knowledgable' instead of 'knowledgeable' which may affect professional credibility.",
"raw_text": "You are a knowledgable assitant on Oxford Economics",
"raw_text_ref": [
{
"line_start": 31,
"line_end": 31,
"confidence": 0.9
}
],
"affected_id": "agent_001"
},
{
"id": "failure_003",
"risk_type": "PLANNING_ERROR",
"description": "Missing validation step for user input quality and spell-checking before processing, leading to potential propagation of errors.",
"raw_text": "",
"raw_text_ref": [
{
"line_start": 19,
"line_end": 32,
"confidence": 0.7
}
],
"affected_id": "task_001"
}
],
"optimizations": [
{
"id": "opt_001",
"recommendation_type": "PROMPT_REFINEMENT",
"description": "Enhance the system prompt to include explicit spell-checking and error correction capabilities. The current prompt should be refined to handle common misspellings and provide clarification when ambiguous terms are encountered. This would improve robustness and user experience.",
"affected_ids": ["agent_001"],
"raw_text_ref": [
{
"line_start": 31,
"line_end": 32,
"confidence": 0.9
}
]
},
{
"id": "opt_002",
"recommendation_type": "WORKFLOW_SIMPLIFICATION",
"description": "Add an input validation and preprocessing step before the main task execution. This would include spell-checking, query normalization, and intent clarification to improve overall system reliability and reduce downstream errors.",
"affected_ids": ["task_001", "input_001"],
"raw_text_ref": [
{
"line_start": 19,
"line_end": 19,
"confidence": 0.8
}
]
},
{
"id": "opt_003",
"recommendation_type": "TOOL_ENHANCEMENT",
"description": "Configure the GPT-4o model with specific Oxford Economics domain knowledge and terminology database to provide more accurate and detailed responses. Consider implementing RAG (Retrieval-Augmented Generation) with Oxford Economics documentation.",
"affected_ids": ["tool_001"],
"raw_text_ref": [
{
"line_start": 49,
"line_end": 49,
"confidence": 0.8
}
]
},
{
"id": "opt_004",
"recommendation_type": "PROMPT_REFINEMENT",
"description": "Implement response quality metrics and feedback collection from users to continuously improve the system's knowledge base and response accuracy. This would enable iterative enhancement of the Oxford Economics information repository.",
"affected_ids": ["output_001", "human_001"],
"raw_text_ref": [
{
"line_start": 20,
"line_end": 20,
"confidence": 0.7
}
]
}
],
"metadata": {
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"resolved_at": "2025-01-27T11:35:54.766346",
"original_trace_length": 4203,
"resolution_method": "enhanced_content_reference_resolver",
"confidence_scoring": true
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"window_info": {
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"chunk_size": 2288,
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"overlap_size": 1144,
"splitter_type": "agent_semantic",
"log_type": "structured_json",
"boundary_used": "json_object_end",
"boundary_confidence": 0.8,
"contains_agent_markers": false,
"contains_tool_patterns": true,
"overlap_with_previous": true,
"global_line_start": 1,
"global_line_end": 53,
"processed_at": "2025-01-27T11:35:22.437186",
"line_mapping_created": true,
"window_total": 4,
"trace_id": "1dca1078-8505-4263-998a-740e7794a94c",
"processing_run_id": "eac673d4"
},
"merge_info": {
"source_graphs": 2,
"merge_timestamp": "2025-01-27T11:35:54.762510",
"window_count": 2,
"merged_entity_count": 6,
"merged_relation_count": 6,
"deduplication_applied": true,
"quality_score": 0.92
},
"processing_info": {
"entity_deduplication": {
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"duplicates_removed": 2
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"relationship_deduplication": {
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},
"failure_detection": {
"total_failures_detected": 3,
"failure_types": ["HALLUCINATION", "AGENT_ERROR", "PLANNING_ERROR"],
"confidence_threshold": 0.7
},
"optimization_generation": {
"total_optimizations": 4,
"recommendation_types": ["PROMPT_REFINEMENT", "WORKFLOW_SIMPLIFICATION", "TOOL_ENHANCEMENT"],
"priority_scoring": true
}
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"skip_layers_threshold": 3,
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}
},
"processing_params": {
"method_name": "production_enhanced",
"batch_size": 3,
"parallel_processing": true,
"merge_method": "hierarchical_batch_with_quality_control",
"optimization_applied": true,
"failure_detection_enabled": true,
"confidence_scoring_enabled": true,
"window_size": 800000,
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"splitter_type": "agent_semantic",
"enhancement_features": [
"spell_checking",
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"comprehensive_failure_detection"
]
},
"schema_version": "2.1.0",
"generation_timestamp": "2025-01-27T12:00:00.000000Z",
"model_used": "gpt-4o-mini-enhanced",
"processing_duration_seconds": 168.4
}
}
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