How do I let AI debug production incidents?
Give it incident runbooks, past postmortems, and live telemetry via MCP. bRRAIn's Incident Response skill plus the graph's past-incident layer lets the agent triage, correlate, and suggest fixes with cited precedent.
Why stock LLMs flop at incident triage
A stateless LLM asked to debug a production incident gets a blank page. It has no runbook, no history of similar alerts, no live telemetry, no knowledge of which service changed recently. What it has is pattern-matching on public incident postmortems — useful as a starting prompt, useless as an operator. Effective AI-assisted incident response requires three inputs the model cannot carry internally: runbooks, past postmortems, and live signals from the stack.
Connecting runbooks, postmortems, and telemetry
bRRAIn's POPE graph ingests runbooks as step-by-step nodes, postmortems as linked cause-and-effect chains, and service maps as first-class structure. Live telemetry — logs, metrics, traces — connects through the MCP Gateway to purpose-built connectors (Datadog, Grafana, CloudWatch, PagerDuty). The Consolidator fuses incoming alerts with graph context so the agent always sees the current state plus the historical pattern. An alert stops being a line of text and becomes a structured query over the environment.
How the agent triages
When an alert fires, the agent walks the graph for the matching service, pulls the runbook, correlates with recent deploys, and queries telemetry for anomalies. It produces a triage memo that cites its sources: "error rate up since deploy 4f8a21 at 14:02; runbook step 3 applies; two prior incidents in 2025 had identical signatures, resolved by rolling back". The Handler keeps the memo grounded in your artefacts rather than generic SRE advice. On-call engineers read a specific, cited first pass instead of starting from zero.
Where humans stay in charge
The agent suggests; the operator decides. Rollback commands, config flips, and scale operations pass through the Security Policy Engine with human approval gates. Once the incident closes, the agent drafts a postmortem from the graph data and the actions taken — human review finalises it, and the postmortem feeds back into the graph as new precedent. The loop compounds: each incident makes the next triage faster because the evidence base grows.
Relevant bRRAIn products and services
- MCP Gateway — connects Datadog, Grafana, CloudWatch, and PagerDuty as first-class telemetry sources.
- POPE Graph RAG / Incident Layer — stores runbooks, postmortems, and service maps as queryable nodes.
- Handler — assembles triage memos with cited runbook and postmortem sources.
- Consolidator — fuses live alerts with graph context in real time.
- Security Policy Engine — gates mitigation actions on human approval.
- Book a demo — watch an alert turn into a cited triage memo in under a minute.