Operations

GenAIOps

Your prototype works on your laptop. It demos well. Then it goes live, the traffic shape changes, the model provider tweaks something upstream, a tool call times out at 3am — and someone has to know what to do about it.

GenAIOps is the run-time operations practice for AI applications: observability tuned to model behaviour and tool-use chains, on-call design for failure modes that don't look like normal outages, runbooks that distinguish a degradation from an incident.

If Coherence is the build-time discipline that keeps intent and implementation aligned, GenAIOps is the run-time practice that keeps the live system honest. See /coherence for the other half.

How it works

The operating model

The operating model has four parts: observability, on-call design, runbooks, and the maturity ladder that ties them together. Treat them as a stack — each layer assumes the one below it works.

Observability

Model-aware signals — token shape, latency-per-step, tool-call success/failure, judge disagreement, retrieval hit rates. The instrumentation that lets you tell what the system is doing, not just that it's running.

On-call design

Failure modes that don't look like normal outages — silent quality degradation, prompt-injection in the wild, a provider rate-limit dropped without warning. Paging rules and severity definitions adapted for "the model got worse without going down."

Runbooks

Decision trees for the failure modes above. A runbook is a one-page document an on-call engineer can read at 3am and act from. Templates for the most common shapes; you adapt them to your stack.

The maturity ladder

Five levels from deployed but not run through continuously evolved. A self-assessment grades your current state; the GenAIOps Maturity Model magnet maps each level to the practices it needs.

Steve is an Ambassador at The Centre for GenAIOps. The operating model on this page is the practice he helps shape there.

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