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OPENREVIEWJun 28, 2026Highlight

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

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.

OPENREVIEWJun 28, 2026Highlight

Observability Patterns for Production AI Systems: Monitoring RAG Pipelines, Vector Databases, and LLM Inference at Scale

The paper identifies five failure modes specific to production AI systems that traditional observability misses. It proposes an observability architecture integrating Prometheus, Grafana, and OpenObserve. Metrics are defined across retrieval quality, vector database health, LLM inference performance, and end-to-end pipeline latency. The framework was validated in a production environment handling 2 million daily queries. It reduced mean time to detection by up to 97% for previously undetectable incidents.

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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

Observability Patterns for Production AI Systems: Monitoring RAG Pipelines, Vector Databases, and LLM Inference at Scale