On cloud incident: auto-generate postmortem structure (timeline, root cause, lessons learned, action items); save to doc store and notify team.
What it does: Webhook (incident report: incident_id, start_time, end_time, affected_services) → extract incident metadata → query cloud logs for event timeline (when did service degrade, first alert, detection, recovery) → LLM generates postmortem structure (summary, timeline, root cause hypothesis, lessons learned, 3-5 action items) → save to Google Drive/Notion → Slack to #incident-postmortems with link.
Configure incident webhook: POST incident details (timestamp, affected services, severity)
Connect doc storage: Google Drive, Notion, or Confluence (credentials)
Configure Slack channel: #incident-postmortems
Test: trigger with sample incident; verify document generated
Apps/nodes: Webhook, code node (metadata extraction), HTTP request (cloud logs API), Claude/OpenAI (postmortem), HTTP request (doc upload), Slack.
Credentials required: Cloud API, doc storage credentials, OpenAI account, Slack workspace.
Difficulty: High | Setup time: 12 minutes.
Business outcome: Incident learning captured immediately → postmortems ↑ thorough, blameless culture ↑, repeated incidents ↓ prevented.
With an active subscription, download the workflow file right from this page or your dashboard.
Choose Import from File and select the downloaded JSON. The full agent graph appears, ready to configure.
Each integration node prompts for credentials on first run. The setup guide lists every credential the agent expects.
Run once with sample input, confirm the expected output, then flip the activate toggle.
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This agent — and every other in the collection — comes with your subscription. One plan, the whole catalog.
Subscribe to downloadMonitor cloud job execution (K8s, Lambda, Cloud Run); on failure, fetch logs, LLM diagnoses root cause (timeout, permission, resource) — alert instantly.