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. Now the billing job keeps crashing.
The first instinct is to blame the model. That instinct burns time. The real failure lives in the handoff.
A handoff happens when intent moves between people. The prompt author knows the goal. The reviewer knows the diff.
The on-call knows neither at 2 a.m. The AI remembers the conversation. The humans do not.
Treat every AI-generated change as a handoff without a memory. Prompt text is not intent. A green test suite is not proof. An incident is the collision of all three.
The sixty-minute clock
Contain first. Investigate second. The first five minutes have one job. Stop the damage.
Roll back the release or flip the feature off. A revert is boring. Boring is fast. Boring is the point.
# Option A: roll back the deployment
kubectl rollout undo deployment/billing
# Option B: disable the feature at the flag service
curl -X POST https://flags.example.com/api/billing-v2 \
-H 'Content-Type: application/json' \
-d '{"enabled": false}'
Debugging begins after containment. Teams often skip this order. They read the diff while users feel the blast.
Minutes five through fifteen belong to evidence. Panic erases context within an hour.
Capture the prompt, the git range, and the test output. One command builds the incident bundle.
#!/usr/bin/env bash
# incident_snapshot.sh: bundle evidence for an AI-change incident
set -euo pipefail
DIR="incident-$(date +%Y%m%d-%H%M%S)"
mkdir -p "$DIR"
git log --oneline -10 > "$DIR/git_log.txt"
git diff HEAD~1..HEAD > "$DIR/last_diff.patch"
echo "$PROMPT" > "$DIR/prompt.txt" # paste the original prompt
echo "$MODEL_OUTPUT" > "$DIR/model_output.txt" # paste the model summary
pytest -q --tb=short > "$DIR/test_output.txt" 2>&1 || true
echo "bundle ready: $DIR"
This bundle is the handoff that never existed. It lets a late responder reconstruct the change. It also exposes gaps in the model's summary.
Minutes fifteen through forty belong to reproduction. Run the failing input against a plain model.
Strip away the scaffolding. A minimal failing case shapes the fix. A free model tier handles this step alone.
No reserved GPU is necessary. The goal is a small repro, not a full solution.
The repro has a second benefit. It re-runs the prompt with fresh eyes. The on-call sees raw model behavior. That signal often beats any log line.
Minutes forty through sixty belong to the decision. Either the fix is obvious, or the revert stays.
Write one paragraph about the failure mode. Update the runbook. The next on-call inherits a clue, not silence.
The runbook to paste into a wiki
Compress the entire clock into six lines. Put them on a page named AI-Change Incident.
# AI-Change Incident
1. Contain. Revert or flag off before reading the diff.
2. Snapshot. Run incident_snapshot.sh.
3. Reproduce. Failing input against a plain model.
4. Decide. Fix now, or keep the revert.
5. Record. One paragraph on the failure mode.
6. Restore. Confirm the state, then hand back.
The compressed version matters more than this article. A pager at 2 a.m. can read six lines. It cannot skim an essay. Make the wiki page the source of truth.
Rehearse before the pager picks you
Playbooks rot without practice. Incident drills usually need reserved infrastructure. That cost stops most teams from running them at all.
The drill here runs on nearly free resources. MonkeyCode is an open-source project with a free tier for incident rehearsal.
Disclosure: This article was prepared as part of MonkeyCode's product outreach.
The current free offer includes ten million model tokens and a free server. Quotas change with product decisions. Verify them before scheduling the year.
The free server is the drill environment. The free tokens cover model calls. One rehearsal per quarter costs almost nothing. That pattern survives budget season.
A drill needs a broken change, a script, and a blind responder.
# drill.sh: arm a staged incident without touching production
git checkout -b drill/billing-ledger
cp broken_billing.py services/billing/ # a known-bad change
git add -A
git commit -m "feat(billing): compact the ledger (drill)"
git push origin drill/billing-ledger
echo "drill armed. pager rotation starts now."
Name the drill after a real incident. Assign a pager who never saw the change. Hide the fix from the room.
Start the clock. Run the six-line runbook for real. Then compare the chosen path against the intended one.
The first drill will expose ugly gaps. That is the point of the drill. The second drill feels calmer. The third one becomes a habit.
Limitations and exclusions
This clock is not universal. Teams without rollback or feature flags need a longer containment window. Teams without logs cannot snapshot anything.
Safety-critical systems require a human approver with real authority. No rehearsal replaces that responsibility.
Teams with noisy alerts need a triage step before minute zero. The clock starts after a real failure is confirmed.
Skip this playbook when every generated line gets human review before merge. Those teams already moved the failure earlier.
They need a review workflow, not an incident one. The two playbooks complement each other. They do not substitute.
Treat the free tier as a moving target. Offers change and quotas shift. The numbers in this article reflect the operator's current check. Re-verify before adopting the drill as a quarterly ritual.
The pattern that survives
AI-assisted teams fail at the boundary between intent and action. The model generates. The human approves.
Neither writes down what the other needs. The incident bundle fixes that boundary. The six-line runbook makes it operational.
The quarterly drill makes it stick. Run the first drill on the free tier. Ten million tokens and a free server are enough to start.
Your future on-call self will thank the team that rehearsed.
This article was originally published by DEV Community and written by Morgan Xu.
Read original article on DEV Community