Enterprise AI systems often do not fail in obvious ways. They continue to run, APIs stay healthy, and applications remain available — yet outputs can still be wrong, weakly grounded, inconsistent, or unsafe. This creates a new operational challenge for teams deploying AI in real-world products and workflows.

This session introduces a practical framework for detecting those silent failures before they reach users. It explores how trust can be treated as a runtime capability through signals such as grounding quality, contextual risk, and output consistency, rather than relying only on static guardrails or offline evaluation.

Designed for teams working on intelligent apps, Copilot-style experiences, and broader enterprise AI adoption, the session will show how to think about trust, governance, and observability as part of production architecture. Attendees will leave with a clearer model for moving from AI pilots to more reliable, production-ready systems.