AI Incident Response: What Happens When the Model Is Wrong

The full executive answer

There is a built loop, and I will describe it exactly as deep as it currently is. Every AI output carries feedback controls — thumbs up, thumbs down, edited, flagged — recorded per module in a dedicated feedback table with optional comments. The down-rated, edited and flagged items are explicitly designed as the candidate pool for new evaluation gold cases: a caught mistake becomes a permanent regression test, so the same class of error is machine-checked on every future build. That is the flywheel — errors convert into gates.

Correction of the artifact itself is immediate: regeneration replaces the stored insight in place, and our staleness system flags derived artifacts when their underlying data changes, so a corrected assessment propagates rather than leaving stale conclusions lying around. Separately, generation quality is monitored continuously in production — every insight gets a deterministic quality score, and a scheduled job alerts operations when average quality drops or provider-fallback rates spike, so systematic degradation is caught by monitoring, not by customers.

What I will not overclaim: the loop today is capture, regenerate, monitor, and convert-to-test-case. It is not yet a formal incident-response runbook with customer SLAs for AI errors, and there is no production history because we are pre-launch — the loop’s throughput starts at zero. The design principle it follows is the AI RMF one: treat wrong outputs as managed incidents that improve the system, not embarrassments to bury.