IncidentLens

**IncidentLens — Evidence-First AI Incident Response & Governed Self-Repair** IncidentLens transforms how engineering teams investigate and respond to production incidents using Meta Muse AI. Built with Muse Wonder 1.0 and Muse Spark 1.3, IncidentLens coordinates six specialized AI agents in a collaborative War Room to investigate application logs, distributed traces, Kubernetes infrastructure, source code, configurations and deployment changes. Unlike conventional AI assistants, IncidentLens does not blindly trust model-generated conclusions. It verifies quoted evidence against original source files, challenges hypotheses through a dedicated Skeptic agent and validates proposed code patches against isolated copies. A deterministic governance engine ensures AI cannot autonomously approve production changes. High-risk modifications require independent human approvals, with veto controls, expiration rules and tamper-evident audit logs. IncidentLens also monitors its own performance. When a regression occurs, its AI Doctor can investigate the failure and restore validated skills under strict safeguards. Its model-continuity system demonstrates recovery from simulated outages through Muse Wonder-to-Spark failover, circuit breaking and an auditable Evolution Ledger. In live Muse evaluations across five synthetic production incidents, IncidentLens correctly identified all five root causes in both solo and multi-agent modes, with 109 of 109 final report citations verified. The vision is to make AI-powered incident response **explainable, verifiable, resilient and accountable**, while keeping humans in control of consequential changes. The project is open-source, with clearly documented testing results and limitations. Production remediation remains simulated, and full zero-downtime recovery is a future objective.