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This guide focuses on Post-Incident AI Summaries for production systems, with practical trade-offs for reliability, security, and cost.
Blank-page postmortems delay learning; ungrounded LLM writeups invent confident fiction. The workable pattern is mechanical: assemble a timeline from PagerDuty, CloudWatch, X-Ray, and deploy markers, ask the model to draft only from those artifacts, then require a human editor before anything is published.
⚡ TL;DR: Export incident timeline JSON (alerts, deploys, graph annotations, chat excerpts). Prompt with “cite artifact IDs or stay silent.” Humans edit impact, root cause, and action items. Never auto-publish. Pair with LLM Incident Runbooks, Deterministic Replay, and Incident ChatOps AI.
Post-Incident AI Summaries: production guidance
text
PagerDuty + CloudWatch alarm history
+ CodeDeploy / ECS deployment events
+ X-Ray / OTel trace highlights
+ Slack #incident pinned messages (sanitized)
↓
timeline.json (immutable artifact)
↓
Bedrock draft (cite-or-silent)
↓
Human edit + approve
↓
Publish to wiki / Blameless
Timeline artifact schema
json
{
"incident_id": "inc-2026-09-11-checkout",
"events": [
{"t": "2026-09-11T05:12:00Z", "type": "alarm", "id": "cw:PayErrorRate", "summary": "Pay 5xx > 2%"},
{"t": "2026-09-11T05:10:00Z", "type": "deploy", "id": "ecs:checkout:17", "summary": "checkout task def 17"},
{"t": "2026-09-11T05:18:00Z", "type": "mitigation", "id": "pd:ack", "summary": "rolled back to td 16"}
],
"metrics": {"impact_orders": 842, "error_budget_burn": "3.2x/1h"},
"forbidden": ["speculate beyond events", "name individuals beyond roles"]
}
typescript
const system = `You draft postmortem sections from timeline.json only.
Every factual claim must cite an event id. If unknown, write "UNKNOWN".
Do not invent root causes. Output markdown sections: Summary, Timeline, Impact, What went well, What went poorly, Action items (owners TBD).`;
const draft = await bedrock.converse({
system: [{ text: system }],
messages: [{ role: "user", content: [{ text: JSON.stringify(timeline) }] }],
});
// store draft + prompt hash + model id for audit (deterministic replay)
Human editing gates
| Section | AI may draft? | Human must confirm |
|---|---|---|
| Timeline | Yes from artifacts | Ordering + missing events |
| Impact | Numbers from metrics only | Customer messaging |
| Root cause | Hypotheses labeled | Final RCA wording |
| Action items | Suggestions | Owners, due dates, priority |
bash
Ground the same way you ground on-call assistants (LLM Incident Runbooks) and keep agent sessions replayable (Deterministic Replay). ChatOps may draft tickets but never auto-remediate (Incident ChatOps AI).
# ✅ Publish path
# draft.md + timeline.json + editor sign-off in PR
# ❌ Model posts directly to the company wiki
Closing checklist
- [ ] Timeline export is automated and immutable per incident
- [ ] Prompts enforce cite-or-silent; unknowns marked UNKNOWN
- [ ] Drafts store model id, prompt hash, and source artifact digests
- [ ] Human editor required before wiki publish
- [ ] Action items need real owners—AI suggestions are not tickets
- [ ] PII/secrets scrubbed from Slack excerpts before model context
Related reading
- LLM Incident Runbooks: Ground On-Call Answers in CloudWatch Signals
- Deterministic Replay: Agent Sessions You Can Debug in Postmortems
- Incident ChatOps AI: Draft Change Tickets Without Auto-Remediating
- AI On-Call Copilots: Suggest Runbooks Without Mutating Production
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