Chimera
A society of AI agents that diagnoses production incidents and challenges its own conclusions.
Chimera is an incident-response system built on a society of AI agents. When an alert arrives from PagerDuty, a webhook or its own metric watcher, a meta-orchestrator designs a team of specialist agents for that kind of failure, while a separate set of adversarial agents tries to pull their diagnosis apart before anything is recommended.
A consensus engine weighs the specialists' confidence against the objections and decides whether the fix is safe to propose or needs a human. Resolved incidents feed a memory of reusable patterns, stored with pgvector, so the next similar incident starts with what the last one taught it.
I built the whole system: the NestJS API with queued incident handling, the live Next.js dashboard that shows the agents thinking in real time, and the Slack and webhook integrations.
The challenge
On-call engineers lose the first minutes of an incident to guessing. A single AI answer is risky because it can be confidently wrong, and a wrong fix in production costs more than a slow one.
What I did
I built a NestJS API where a meta-orchestrator designs a team of specialist agents for each incident and adversarial agents challenge every diagnosis. A consensus engine weighs confidence against dissent and escalates to a human when it is not sure. Incidents run on BullMQ queues, agents talk over a message bus, and every step streams to a Next.js dashboard through Socket.IO. Resolved incidents are stored with pgvector so later incidents of the same kind start with what was learned.
The result
A working system that goes from alert to a reasoned, challenged resolution, shows its thinking live and knows when to ask a human. Alerts come in from PagerDuty, a generic webhook or built-in metric rules, and updates go to Slack.
My part
- Backend architecture
- Multi-agent orchestration
- Realtime dashboard
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