Equips AI agents with a structured approach to incident investigation, leveraging runbooks for evidence collection, decision-making, and generating comprehensive reports to eliminate speculative root causes.
Sponsored
This tool provides a runbook-driven framework for AI agents to conduct systematic backend incident investigations. It transforms repetitive troubleshooting patterns into executable runbooks, enabling agents to gather evidence in a predefined order instead of freely guessing. The system includes components for selecting appropriate runbooks based on incident patterns, executing investigation steps through various adapters (like Langfuse, PostgreSQL, and Redis), normalizing collected evidence into structured findings, and a decision engine that maps evidence combinations to conclusions and recommended next actions, all exposed via an MCP server entrypoint.
主要功能
01Evidence normalization for structured findings
02Decision engine for mapping evidence to conclusions and actions
032 GitHub stars
04MCP server entrypoint for AI tool integration
05Executor for orderly adapter calls defined by runbooks
06Runbook selector for matching incident patterns
使用案例
01Automating investigation of backend incidents where expected side effects did not occur
02Troubleshooting common issues like stale cache or abnormal persisted business state
03Empowering AI agents to perform systematic, data-driven incident diagnosis