The First Hour: An On-Call SOP for AI-Generated Code
Most teams plan for smooth AI handoffs. Almost none plan for the broken one. This article defines a sixty-minute playbook for AI changes that fail in production. Every step fits on one page. Teams can rehearse it with free model tokens and a free server. The pager fires at 2 a.m. A feature shipped the previous day. An AI assistant wrote most of the diff. Tests passed. The preview looked clean.…
When an AI-generated code change fails in production, teams often jump to blame the model without understanding the root cause. This article provides a sixty-minute playbook to handle such incidents effectively. The first five minutes are dedicated to stopping the damage by either rolling back the release or disabling the feature.
This containment phase prevents further harm before any investigation begins. During investigation (minutes five through fifteen), capture essential evidence such as the prompt, git range, and test output to create an incident bundle using the incident_snapshot.sh script. In the reproduction phase (minutes fifteen through forty), run the failing input against a plain model to identify a minimal failing case that can guide the fix.
The decision phase (minutes forty through sixty) involves choosing between fixing the issue or keeping the revert. Finally, the runbook is updated with a paragraph summarizing the failure mode for future reference. Practicing this incident drill with resources like MonkeyCode, which offers free tokens and a server, helps teams prepare for on-call situations at 2 a.m. when AI-generated code changes may cause unexpected failures.
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