As AI systems evolve from simple chat experiences into complex, multi-step agents, evaluating quality becomes increasingly challenging. In data-sensitive and highly governed environments, teams must balance innovation with reliability, safety, and compliance requirements.
This talk explores practical approaches for building continuous evaluation capabilities using production signals, testing frameworks, and automated feedback loops. We'll discuss lessons learned operationalizing evaluations at scale and how evaluation-driven development can help teams build more trustworthy AI systems.
Attendees will leave with patterns they can apply to their own AI platforms and products.