AI Postmortem Generator for Engineering Teams

What is a Postmortem Generator?

A postmortem generator is a tool that turns raw incident notes — timeline entries, root cause hypotheses, mitigation steps, and open questions — into a structured, shareable postmortem document. Instead of spending hours in a Google Doc at the end of a long incident, you fill out a form and the tool writes the document for you.

Good postmortems are blameless, honest, and actionable. They document what happened, what is still uncertain, and what will be done differently. A generator enforces that structure so nothing important gets skipped under pressure.

How AutoBrief Generates Your Postmortem

AutoBrief collects your incident data in one structured form: what broke, when it started, who responded, contributing factors, what fixed it, and what remains uncertain. It then generates a complete postmortem with:

• A structured timeline of events in chronological order
• Root cause analysis with explicit space for uncertainties
• Impact assessment covering user-facing and SLA implications
• Contributing factors organized by category
• Concrete action items formatted for Jira, Linear, or any tracker
• A separate "what we are still investigating" section

The same form also generates an incident postmortem template for structured reviews, a status page update for customers, and a runbook delta for your documentation team.

Example Output

Here is an abbreviated sample of what AutoBrief generates from a database failover incident:

Postmortem: Database failover delay — 2024-01-15
Severity: SEV-2 | Duration: 47 min | Affected: Payment service

Timeline
  09:14 — Elevated error rate detected on payment service
  09:17 — PagerDuty alert fires; on-call SRE begins investigation
  09:31 — Root cause identified: read replica lag causing timeouts
  09:45 — Traffic rerouted to primary; error rate normalizes
  10:01 — Monitoring confirms stable; incident marked resolved

Root Cause
  The primary read replica fell behind due to a large batch
  query running without a rate limit during a traffic spike.
  The query was scheduled for "off-peak" hours but the
  off-peak threshold had not been updated since Q3 traffic grew.

Contributing Factors
  · No alert existed for replica lag > 30 seconds
  · Batch job scheduler used stale traffic baseline
  · Runbook did not cover replica failover procedure

Action Items
  □ Add replica lag alert at 30s threshold  (SRE, next sprint)
  □ Rate-limit batch queries in worker service  (Platform, 2w)
  □ Update runbook with replica failover steps  (SRE Lead, 1w)
  □ Review off-peak scheduler thresholds quarterly  (Platform, 1w)