Runbooks + RAG: How I Gave My AI SRE Agent the Context It Was Missing

In my previous article, I described building a semi-autonomous SRE agent that investigates incidents and drafts fixes for human approval. This one is about the part of that build I have not told yet: how the agent knows what it knows, and how many tries it took to get there.

There is a pattern to AI DevOps demos. The agent gets a clean alert, queries a metric, finds an obvious anomaly, and produces a confident diagnosis. Everyone applauds. Then someone points it at a real environment and it confidently recommends restarting a service that has been deprecated for two years.

The gap between the demo and production is not intelligence. It is context.

An LLM knows what Kubernetes is. It does not know that your payments-api has a flaky liveness probe everyone ignores, that the checkout team owns the retry policy, or that the last three "database incidents" were...

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