Your agent is live. The service is healthy. But are its answers getting better? By connecting production traces, curated data, and evaluations, your team can find failures, choose improvements, and prove the next version works before it reaches users.

A first deployment is still a milestone. Then the hard questions begin: How do we keep outputs accurate and customers happy? What will it cost us? After a few iterations, teams can spend more time maintaining tools and coordinating handoffs than improving the application itself.

Consider what happens when the AI engineers who trained a model hand it off to an application or site reliability engineering (SRE) team. The engineers understand what “good” looks like; the production team owns the running service. From there, they may look at different systems.

Application traces land in a dashboard built for uptime. The on-call engineer sees a healthy service, while the research team learns little about answer quality or user sentiment. The evaluation suite drifts as user behavior changes, but no one owns refreshing it. Researchers, AI engineers, and business leads return to separate dashboards for the next release.

The on-call engineer sees a healthy service, while the research team learns little about answer quality or user sentiment.

Start by asking who can turn a production failure into an evaluation case, and who owns keeping that case current. If the answer requires copying context between teams, that’s where you’re lost in the handoffs..

The AI loop follows a familiar cycle: run a model or agent, observe its behavior, curate the signal into data, improve the system, evaluate the result, and repeat.

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