In the era of AI, a “working agent” is only as good as the data foundation underneath it. Ontologies are discussed everywhere around us, but what value they add often remains unclear.

In this deep dive, we’ll go from raw source data to an AI ready Microsoft Fabric data foundation and finish with a data agent that can answer meaningful business questions—reliably and repeatably.
We’ll build a well architected Lakehouse using a medallion approach (Landing Zone → Bronze → Silver → Gold) and show how those layers translate into robust ingestion, transformation, historization, and curated consumption for analytics and agents. From there, we’ll connect a semantic layer and discuss why a clean semantic model and clear metadata are critical for agent accuracy and adoption. Next we will discuss ontologies on top and next to semantic models as alternative as well as addition.
Finally, we’ll bring it together with Fabric Data Agents concepts, tools, context, configuration and structured data access patterns, so you understand what it takes to move beyond demos and build something you can actually use in real projects.