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Overseer creates incidents from errors and log patterns, deduplicates by fingerprint, ranks by severity, and dispatches AI agents to investigate and fix — all automatically.
An error occurs in the browser or is detected from log analysis. The SDK flushes its 15-second DVR buffer and sends the incident payload with full context: error, stack trace, replay clip, breadcrumbs, and user info.
Each error gets a fingerprint derived from stack trace + message pattern. If an open incident with the same fingerprint exists, the event count is incremented instead of creating a duplicate.
AI ranks each issue by blast radius (affected users), severity, recency, and frequency. Issues scoring ≥ 80 are flagged as critical signals and trigger immediate alerts.
For high-severity incidents, an AI agent is auto-dispatched. The agent analyzes the codebase, identifies root cause, suggests affected files, and generates a fix.
Critical issues can trigger the autofix pipeline: analyze → branch → patch → PR → human review. Lower-priority issues are converted to tickets with full context attached.
When resolved, the incident is archived. The AI remembers the fix pattern so similar future errors can be resolved faster.
Incidents are stored in a two-tier system for fast access and long-term archival:
Recent incidents live in Redis (Upstash) with 7-day TTL. Indexed by reference ID, digest, fingerprint, and issue ID for fast lookup.
Full incident payloads (including replay clips) are permanently archived in R2. Lookup falls back to R2 when Redis TTL expires.
Critical incidents post to your Slack channel with title, severity, affected users, and a link to the replay.
Digest emails for medium-priority issues. Instant emails for critical signals.
The Codmir AI companion adjusts alert intrusiveness based on your dismiss patterns. Learns what you care about.
Query incidents directly from your IDE using the Overseer MCP tools:
AI-powered investigation and autofix. Free to start.