# Model operations control plane kit

Use this kit when an AI service depends on multiple models, providers, routing tiers, fallback paths, release gates, and runtime owners. The goal is to keep model operations inspectable and rehearsed before incidents happen.

## Control-plane scope

- Routing policy: workflow class, primary model, fallback chain, cost budget, latency target, and approval threshold.
- Runtime health: provider status, latency, cost drift, fallback rate, quality regression, and reviewer disagreement.
- Incident triage: failing layer, customer impact, release version, fallback state, owner, containment action, and validation signal.
- Fallback drills: provider outage, latency spike, cost runaway, unsafe output pattern, and model-quality regression.
- Release confidence: evaluation result, known regression, rollback path, approving owner, and monitoring window.

## Operating cadence

Review the control plane weekly for open incidents, fallback performance, cost and latency drift, and release-readiness gaps. Run fallback drills monthly until the team can prove that degraded mode works without ad hoc decisions.

## Decision rule

Do not promote a model or prompt change unless routing policy, rollback evidence, incident owner, fallback behavior, and customer-impact monitoring are all current.