IncidentMind: Token-Budget Multi-Agent Autonomous Incident Response Using MCP Orchestration, HydraDB Temporal Memory, and Tri-Tier Model Inference with 98% Token Reduction and 91% Fix Accuracy
中文标题: IncidentMind：基于MCP编排、HydraDB时序记忆与三层模型推理的令牌预算多智能体自主事件响应系统，实现98%令牌缩减与91%修复准确率
英文摘要
IncidentMind is a token-budget multi-agent system for autonomous root cause analysis of production AI failures. It pre-syncs Slack, Confluence, and Jira into a HydraDB temporal knowledge graph via MCP, converting all agent queries into a single graph traversal. A tri-tier inference strategy uses minilm-l6 for sync-time tasks, quantized Llama-3-14B for agent reasoning, and GPT-4o-mini only when confidence falls below 85%, reducing per-incident cost from $1.50 to $0.003. Structured token budgeting compresses 50,000 raw log tokens to 1,050 tokens (98% reduction). Across 847 production incidents, IncidentMind achieved 91% fix accuracy and reduced mean time to detect from 4.2 hours to 3 minutes.
中文摘要
IncidentMind 是一个面向生产级AI故障自主根因分析的令牌预算多智能体系统。它通过MCP将Slack、Confluence和Jira预同步至HydraDB时序知识图谱，使所有智能体查询简化为单次图谱遍历。三层推理策略（同步时使用minilm-l6，智能体使用量化Llama-3-14B，仅当置信度低于85%时调用GPT-4o-mini）将单次事件成本从1.50美元降至0.003美元。结构化令牌预算将50,000条原始日志令牌压缩至1,050条（缩减98%）。在847个生产事件的评估中，IncidentMind实现了91%的修复准确率，并将平均检测时间从4.2小时缩短至3分钟。
关键要点
Pre-syncs Slack, Confluence, Jira into HydraDB temporal knowledge graph via MCP, reducing all agent queries to a single graph traversal.
通过MCP将Slack、Confluence和Jira预同步至HydraDB时序知识图谱，将所有智能体查询简化为单次图谱遍历。
Tri-tier inference (minilm-l6 → quantized Llama-3-14B → GPT-4o-mini) reduces per-incident cost from $1.50 to $0.003.
三层推理（minilm-l6 → 量化Llama-3-14B → GPT-4o-mini）将单次事件成本从1.50美元降至0.003美元。
Structured token budgeting compresses 50,000 raw log tokens to 1,050 tokens (98% reduction).
结构化令牌预算将50,000条原始日志令牌压缩至1,050条（缩减98%）。
Achieves 91% fix accuracy and reduces MTTD from 4.2 hours to 3 minutes across 847 production incidents.
在847个生产事件中实现91%的修复准确率，并将平均检测时间从4.2小时缩短至3分钟。