Giving an AI agent access to a database is easy. Getting it to understand what the data actually means is much harder.

Ask an agent, "Which product grew the most last quarter?" and it can generate SQL, execute it and return a confident answer. But did it understand what "grew" means? Did it choose the correct date? Did it account for returns? Did it use the business definition of revenue?

This session explores the gap between data access and data understanding when building AI agents for analytics. Through practical examples and live demonstrations, we will see how an agent can produce technically valid queries and still reach the wrong analytical conclusion.

We will then explore how semantic context, metadata, business definitions, structured tools and validation can ground AI agents in trustworthy data. The focus is not on building another chatbot over a database, but on understanding what an agent actually needs to become a useful analytical partner.

Attendees will leave with practical architectural patterns for building AI systems that reason over data with more context, transparency and trust.