Giving an AI agent access to a database is easy. Giving it the right understanding of that data is much harder.

Ask an agent, "What were our best performing products last quarter?" and it may produce a confident answer. But which definition of revenue did it use? Which date did it filter on? Did it account for returns? Did it understand what "best performing" means in the business context?

This session explores the gap between data access and data understanding when building AI agents for analytics. We will look at how semantic models, metadata, business definitions and governed context can help agents reason about data more reliably.

Using practical examples, we will walk through common failure cases where an agent has technically valid access to data but still produces the wrong analytical conclusion. We will then examine architectural patterns for grounding agents in trustworthy business semantics, including how semantic layers can work alongside modern AI and data platforms.

The goal is not to build another chatbot over a database. It is to understand what an AI agent actually needs to become a useful analytical partner rather than a very convincing source of incorrect answers.