AI Agent Incident Review Template

Pass or fail

An agent incident review should reconstruct the task, inputs, model decisions, tool calls, human interventions, impact, and prevention test. The practical answer to "AI agent incident review" is a decision rule: write the review without blaming the model as a catch-all cause and close it only when a regression check exists. A credible operating reference should reveal when it does not apply as clearly as when it does.

Scope the decision

The review is complete when the system can detect or prevent the same class of failure. Incident quality determines whether production data compounds into reliability or disappears into chat threads. Document both the expected path and the evidence that would make the team stop, narrow, or redesign it.

The checklist

1. Assign production ownership for incident response

A named operator owns the prompts, data, evals, incidents, and retirement decision. incident response is not production-ready when everybody can use it but nobody is accountable for its failures.

2. Build the eval before autonomy for incident response

Create representative tasks, expected outcomes, and unacceptable failures before granting more permissions. Incident quality determines whether production data compounds into reliability or disappears into chat threads. A demo proves possibility; an eval set shows whether the behavior survives variation.

3. Bound tools and irreversible actions for incident response

Give each tool the narrowest useful permission and route irreversible actions through approval. Write the review without blaming the model as a catch-all cause and close it only when a regression check exists. Review the tool-call trace, not only the final answer.

Review signals

The scorecard for incident response should track incident severity, time to detection, time to containment, plus repeat incident rate and regression coverage. Put the count, cohort, period, and owner next to every result so a reviewer can reconstruct the decision.

1. incident severity

Keep an uncertainty note beside incident severity when the sample is small, attribution is partial, or classification needs judgment. Precision should match evidence.

2. time to detection

For time to detection, publish the event definition, observation window, exclusions, and system of record. Review the underlying records when the result changes materially.

3. time to containment

Use time to containment as a decision signal only after the team agrees which cohort it describes. Keep the count beside the rate and annotate process changes.

4. repeat incident rate

Assign repeat incident rate to the operator who can change its upstream causes. A dashboard owner without operating authority cannot close the loop.

5. regression coverage

Set a baseline for regression coverage before the intervention and retain a comparable holdout or prior cohort when practical. Avoid retrospective targets.

Red flags

Review missing the full trace, fixing the symptom only, and keeping the lesson outside the eval suite before expanding incident response. Each can distort the apparent result or create an impact larger than the narrow workflow suggests.

Failure 1: missing the full trace

Use missing the full trace to inspect incentives as well as execution. Teams often reproduce the behavior a volume target quietly rewards.

Failure 2: fixing the symptom only

Name the customer-facing consequence of fixing the symptom only and the recovery owner. Internal correction is incomplete when trust or data remains affected.

Failure 3: keeping the lesson outside the eval suite

Detect keeping the lesson outside the eval suite with one leading signal and one raw-record check. The owner should be able to pause the affected cohort without waiting for a quarterly review.

Run the first review

Use the template on the most recent escaped error and add one new eval case before closing it. Schedule the follow-up before launch so weak or inconvenient results cannot disappear into the backlog.

Review question: did the work improve incident response, or did it only increase activity around AI agent incident review? Keep the next change tied to the observed constraint and preserve the evidence that supports it.

Connected reading

Continue through AI agents for operators, AI agent evaluation scorecard, and agent trust starts with sandboxes. These pages carry the adjacent concepts, examples, and operator context used by this framework.

Sources and methodology

Primary references: Anthropic: Demystifying evals for AI agents, NIST: AI Risk Management Framework, and Model Context Protocol: Security best practices.

Method note for AI Agent Incident Review Template: this AI-assisted operator draft uses the linked primary sources, existing first-party frameworks on this site, and a no-fabricated-benchmarks rule. Verify current official guidance before making legal, compliance, security, financial, or high-volume operational decisions.