Most canary analysis is a threshold check: error rate > X%, latency > Yms, done. I wanted to see what happens if you replace that with something closer to how a human on-call engineer actually decides — read the logs, check the metrics, look at the events, and weigh them against each other.
So I built an AI-powered metric plugin for Argo Rollouts that can call an agent over A2A: a Lead Orchestrator agent that fans out to three specialists — a Log Analyst, a Metrics Analyst, and an Events Analyst — each independently investigating the canary vs. the stable version. They report back, debate, and the orchestrator resolves any disagreement into a single promote/abort verdict, running on Gemini via Google’s Antigravity agent framework, with some agents sandboxed on GKE Autopilot’s Agent Sandbox (gVisor) for isolation.
The most interesting part wasn’t the happy path — it was the debugging. A broken release gets caught in under a minute: two agents vote to promote based on clean resource metrics, but the Log Analyst — the one that actually read the logs — vetoes it, and the orchestrator sides with the evidence.
Multi-agent systems earn their keep when they’re allowed to disagree with each other, not just when they’re all pointed at the same task in parallel.
Check out the source code
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