Operate the AI service

Operate the outcome, not just the model

A production service review begins with the accepted outcome, eligible population, baseline, target, adoption indicator, operating owner, metric owner, verifier, and attribution limits. Pair them with reliability, safety, latency, cost, human-review load, exception rate, and change history. Good model quality cannot compensate for non-adoption or negative economics.

Define service objectives that reflect user and business impact as well as infrastructure. Monitor the decision route: source availability, context quality, model behavior, tool behavior, human review, effect result, and source-of-truth readback. This makes it possible to localize failure without treating every incident as model drift.

Turn change into a controlled decision

Model upgrades, prompt changes, new data sources, policy revisions, tool versions, and workflow changes can invalidate evidence. Use the typed dependency graph to identify affected evaluations, artifacts, and operating controls, then rerun the relevant checks. The graph routes review; owners and gates still decide.

Review full cost and capacity, including review queues, support, incidents, regressions, and change work. Define cost ceilings and saturation signals before growth. A service that saves task time but creates excessive review or support burden may need constraint or retirement.

Preserve negative and retirement evidence

Record continue, constrain, scale, pause, or retire with evidence, owner, limitations, and next review. Preserve stopped experiments and retired behavior so the organization does not repeat the same failed assumption after staff or model changes.