You’ve Been Given an AI Agent That Answers Questions About Data. How Do You Know You Can Trust It?

AI agents are increasingly becoming the interface for enterprise data: users ask questions in natural language and receive ready-made answers. However, analysts remain responsible for their accuracy.

The key challenge is not frequent failure, but silent failure: an agent may produce a plausible answer to a slightly different question by dropping a filter, changing the reporting period, or altering the aggregation level. Such errors are difficult to detect with conventional validation methods.

This talk explores how to define acceptance criteria, design an effective validation process, and establish checks that make agent behaviour verifiable rather than merely convincing.

We will explain why every answer must be explainable and auditable, distinguish key failure types, and show how to build a representative acceptance test suite. Attendees will receive a practical checklist for evaluating and deploying enterprise AI agents.