IncidentCommander

Overview

IncidentCommander is an AI-powered incident response automation agent that detects production issues and orchestrates immediate response across multiple platforms. When a critical incident is identified in Sentry, the agent instantly creates tracking tickets, establishes communication channels, pages on-call engineers, and provides intelligent root-cause analysis—all in seconds. This eliminates manual coordination delays and ensures consistent, rapid incident triage for engineering teams.

How It Works

1. Get Issue Details (Sentry) — Retrieves comprehensive incident data including error rates, stack traces, and affected services from Sentry
2. Get Trace Details (Sentry) — Pulls the complete distributed trace to pinpoint failure points across microservices
3. Create P0 Incident Ticket (Linear) — Automatically generates a priority-0 tracking issue in Linear with full Sentry context and metadata
4. Create Dedicated Channel (Slack) — Spins up a dedicated #incident-YYYY-MM-DD-[service] Slack channel for real-time collaboration
5. Post Incident Kickoff (Slack) — Publishes an initial message with Sentry links, Linear ticket, and diagnostic summary
6. Generate AI Diagnosis (Claude Sonnet) — Analyzes traces and error patterns using advanced LLM reasoning to generate a root-cause hypothesis
7. Post AI Analysis (Slack) — Shares the intelligent diagnosis to the incident channel as an investigation starting point
8. Invite Responders (Slack) — Automatically adds on-call engineer, service owner, and incident commander to the channel
9. Check Recent Releases (Sentry) — Correlates the incident with recent deployments to identify potential culprits
10. Update Tracking Issue (Linear) — Continuously updates the incident ticket with investigation findings and deployment correlation data

Key Features

• Zero-Delay Incident Response — Automatically coordinates across Sentry, Linear, and Slack in seconds, eliminating manual setup
• AI-Powered Root-Cause Analysis — Leverages Claude Sonnet to generate intelligent hypotheses based on error traces and patterns
• Centralized Incident Hub — Creates dedicated Slack channels with all stakeholders pre-invited and equipped with context
• Deployment Correlation — Automatically checks recent releases to identify whether the incident coincides with a new deployment
• Real-Time Synchronization — Keeps Linear tickets and Slack channels in sync as the investigation evolves

Use Cases

• SaaS Platform Outages — When a critical database or API service fails, the agent immediately pages the team, creates a war room, and provides initial diagnostics to accelerate Mean Time To Resolution (MTTR)
• Error Rate Spikes — Upon detecting unusual error surges in Sentry, the agent gathers distributed traces and deployment history to help teams understand whether it's a code regression or infrastructure issue
• Multi-Service Incident Investigation — For incidents spanning multiple microservices, the agent pulls correlated traces, documents the timeline in Linear, and assembles the right people in a Slack channel
• Post-Deployment Issues — When incidents occur shortly after a release, the agent identifies the correlation and highlights it in both the incident channel and tracking ticket, streamlining rollback decisions