AI agent incident response runbook: contain, investigate, recover
A practical runbook for autonomous-system incidents: kill switch, evidence capture, replay, policy updates, and staged recovery.
Agent incidents move quickly, so response plans should be specific: contain execution, preserve evidence, assess blast radius, and safely resume operations.
Key takeaways
- Containment starts with runtime controls, not model retraining.
- Evidence preservation is critical for root cause and compliance.
- Recovery should include staged re-enable with monitoring.
Implementation checklist
- Trigger fleet kill switch for state-changing actions.
- Export audit timeline and policy state snapshot.
- Re-enable actions gradually with tightened policies.
People also ask
What is the first action during an agent incident?
Stop further side effects using a centralized execution control such as a kill switch or restrictive override policy.
What evidence should teams collect immediately?
Decision logs, policy versions, tool call sequence, actor context, and external effect traces.
How do teams avoid repeated incidents?
Run replay analysis, update policies, and validate controls with scenario-based tests before full reactivation.
Related: AI agent kill switch best practices for incident response, How to audit AI agent decisions (and prove controls worked).
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