Incident response for AI-generated code: the on-call engineer's complete guide

AI-generated production code can complicate incident response because on-call engineers may need to debug unfamiliar logic, including plausible but invalid APIs, business logic errors, security flaws, and dependency mismatches such as slopsquatting risks. The guidance recommends keeping humans accountable for code acceptance, review, deployment, and incident coordination, while recording AI provenance—including the agent, prompt, and approver—in commits and audit trails. During incidents, teams should identify whether AI contributed to the failing change, validate imports and dependencies, review documentation and telemetry, assess rollback safety, and revert changes that cannot be adequately understood or safely patched. It also advocates human approval gates, automated static analysis, security and dependency scans, AI-specific runbooks and alert metadata, and separate tracking of detection and resolution metrics. Post-incident reviews should be blameless, classify recurring AI-related failure modes, preserve relevant AI context, and use findings to improve safeguards; the source additionally promotes incident.io tools for timeline capture, automated investigation, call transcription, drafted fixes, and post-mortems, while stating that humans must review all proposed code changes.

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