AI AGENTS

On-Incident-Close Action Item Extractor

Triggers when a PagerDuty incident is resolved, reads the timeline and chat thread, drafts concrete remediation action items, and files them in Linear linked back to the incident.

CategoryAI Agents
Enginepaperclip
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerPagerDuty incident resolvedPagerDutyPagerDuty
  • ActionPull incident timeline and Slack discussionSlack
  • LogicDrop vague or duplicate action suggestions
  • ActionFile concrete action items as linked Linear issuesLinearLinear
  • OutputPost recap to the incident Slack threadSlack

What it does

The moment an incident resolves is when context is freshest and action items are most accurate — yet teams wait days to write them up, by which point the details have decayed. This agent fires the instant a PagerDuty incident is resolved, reads the incident timeline and the resolution discussion, and drafts specific, owner-assignable remediation action items. It files each as a Linear issue tagged to the incident so the postmortem starts with real follow-ups already on the board.

When to use it

Use it for any team that wants postmortem action items captured automatically at resolution time, not reconstructed from memory a week later.

How it works

A PagerDuty incident-resolved event triggers the agent. It pulls the incident timeline and notes from PagerDuty and the linked Slack discussion thread. The agent drafts discrete action items with proposed owners and severity. A logic step drops vague or duplicate suggestions, keeping only concrete, actionable items. Each survivor becomes a Linear issue linked to the incident, and a recap is posted to the incident's Slack thread for review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PagerDutyIncidents, on-call, escalations.
  2. 2
    Connect SlackChannels, DMs, threads, mentions.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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