Postmortem AI-Drafted

Agent-assisted writing.

Overview

The first hour after an incident is the highest-value time for capturing what happened, and also the hour the team is most exhausted. AI-drafted postmortems split that work: the agent gathers timeline, impact, and contributing factors; the human refines, corrects, and owns the conclusions.

• Timeline assembly. Agent collects Slack messages, alerts, deploy events, and incident actions into a single timeline; saves hours of manual reconstruction.
• Impact quantification. Customer impact and revenue impact estimated from telemetry; the human verifies the numbers rather than calculating them from scratch.
• Contributing factor draft. Agent proposes initial root causes from the timeline; the human confirms, corrects, or replaces them with actual analysis.
• Action item suggestions and template consistency. Patterns from similar past postmortems propose action items; the team's template ensures comparable documents across incidents.

The approach

Human-in-the-loop is non-negotiable. The agent drafts, the human reviews critically, and the postmortem owner remains the accountable name. Speed without rigor produces postmortems that nobody trusts.

• Agent has scoped read access. Slack, monitoring, deploy systems; access boundary documented and audited so the agent operates within an explicit envelope.
• Human reviews the draft critically. The postmortem owner reads with skepticism; agent hallucinations and missed context are caught here, not later.
• Confirm root causes by hand. The human confirms or corrects the agent's analysis; root cause naming stays human accountability.
• Refine action items into commitments. Agent suggestions get effort estimates, owners, and deadlines added; "the agent suggested X" is not an action item.

Why this compounds

The combination keeps paying back: postmortems get written instead of skipped, the agent learns the team's patterns, and engineers trust the document because the human signed it rather than rubber-stamped it.

• Faster postmortems. First draft takes minutes instead of hours; the team writes more of them and writes them sooner after the incident.
• Higher completion rate. Lower friction means more postmortems finish; the team's incident record becomes complete instead of selective.
• Cross-incident learning. Agent surfaces patterns across postmortems; recurring root causes get visible faster than human review alone produces.
• Better data hygiene. The agent depends on clean telemetry; the discipline forces investment in observability that pays back beyond postmortems.

AI-drafted postmortems are exactly what Nova AI Ops is built for. Agent-drafted PMs, human-confirmed, with cross-PM pattern recognition built in.