Every 10 minutes: measure cloud log volume, detect spikes (>2x baseline), LLM analyzes top errors and likely cause — alert to #log-alerts.
What it does: Schedule (every 10 min) → query cloud logs (AWS CloudWatch, GCP Cloud Logging, Azure Monitor) for error rate/volume → code detects anomaly: current > 2x baseline? → IF spike → fetch top errors + services generating them → LLM diagnoses (which service failing, error type, severity: cascade, new bug, traffic spike) → Slack to #log-alerts.
Connect cloud logs: AWS CloudWatch, GCP Cloud Logging, Azure Monitor (API endpoint + credentials)
Set baseline: measure normal error rate (automatic rolling 7-day avg)
Configure spike threshold: multiplier (default 2x = 100% increase)
Set Slack channel: #log-alerts
Apps/nodes: Schedule, HTTP request (logs API), code node (anomaly math), Claude/OpenAI (diagnosis), Slack.
Credentials required: Cloud logs API key, OpenAI account, Slack workspace.
Difficulty: Easy | Setup time: 6 minutes.
Business outcome: Catches error cascades, service issues, traffic spikes instantly → incident response ↑ faster, MTTR ↓ 50%.
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 downloadOn cloud incident: auto-generate postmortem structure (timeline, root cause, lessons learned, action items); save to doc store and notify team.