Most AI agent projects don't fail because of the model. They fail because the data has no shared meaning.

Two agents in the same company define "customer" differently. One reads revenue as gross, another as net. They produce contradictory outputs. Nobody catches it until something breaks.

This session is about that problem. Why it happens, what's missing, and how to fix it - using Microsoft Fabric IQ as a hands-on example.

You'll leave with:
1. A plain-language understanding of semantic layers and ontologies
2. Why this is now infrastructure, not a nice-to-have, for any agentic AI system
3. How the Fabric IQ Ontology item works, shown with a live demo on realistic business data
4. The three data foundation mistakes that most reliably break agent behavior
5. A starting point you can take back to your own environment

The session draws on real Fabric Data Agent deployment patterns and is structured for data engineers, developers, and architects who are building AI agent solutions and want the foundation in place before they scale.