Evaluation Drift

AI systems decay in production. This is not a defect in the models — it is an expected consequence of deploying machine learning systems in environments that change. User behavior changes. Upstream data changes. Business requirements change. The distribution of production queries drifts away from the distribution the system was evaluated against.

The failure is not the drift. The failure is not detecting the drift before users detect it for you.

Most enterprise AI teams invest heavily in pre-deployment evaluation and underinvest in ongoing production monitoring. This paper argues the opposite allocation: a lightweight pre-deployment eval and a rigorous ongoing monitoring practice is more valuable than an exhaustive pre-deployment eval with no ongoing monitoring.

This whitepaper describes the evaluation infrastructure Tenten AI implements for production AI systems, the metrics that reliably detect degradation before it becomes user-visible, and the operational cadence for maintaining evaluation coverage as the production environment evolves.