Most teams still run incident reviews for AI systems the way they ran them for REST APIs in 2017. They collect logs, write a timeline, and stop at "human error" or "model hallucinated." That gives you closure, not improvement.
A good review for agentic systems has one required output: new constraints in code and tests. If the incident happened because tool output was malformed, you should leave with a stricter schema validator. If the incident happened because an agent retried the same failing path six times, you should leave with a bounded retry policy and a circuit breaker.
The review format that works
- User-visible failure first: what exactly broke for the customer.
- Control-plane failure second: where routing, prompts, tools, or state management allowed it.
- Preventive artifacts: one new eval, one new runtime guard, one new dashboard alert.
If a review ends without those three artifacts, it was a meeting, not an engineering mechanism.
Takeaway
Incidents are expensive tuition. Pay once. Convert each one into a testable guardrail so your system gets harder to break every week, not just better narrated in Slack.
