In this video, we'll explore how to build a powerful multi-source AI assistant by extending a Fabric Data Agent with a Microsoft Foundry Agent. We'll walk through how a Fabric Data Agent can answer complex questions over a Fabric Ontology, traversing relationships between Drivers, Trips, Loads, Customers, and Terminals. Then, we'll see how inside Microsoft Foundry, an agent can combine that structured enterprise data with an Azure AI Search vector index of company documentation, allowing a single agent to query both operational data and unstructured policy documents. We'll demonstrate querying live operational data, calculating load values for in-progress trips, and answering company policy questions, showing how Foundry orchestrates these multiple knowledge sources together.
In this video we'll take a look at a Microsoft Fabric Data Agent extended by integrating it into a Microsoft Foundry Agent to create a powerful multi-source AI assistant.
First, we explore how a Fabric Data Agent can answer complex questions over a Fabric Ontology, including queries that traverse multiple entity relationships like Drivers, Trips, Loads, Customers, and Terminals.
Then we take things further.
Inside Microsoft Foundry, we'll see an agent that combines multiple knowledge sources:
• The Fabric Ontology Data Agent for structured enterprise data
• An Azure AI Search vector index containing company documentation
This allows a single agent to answer questions from both structured data and unstructured documents.
We demonstrate:
• Querying operational data through the Fabric Ontology
• Calculating load value for trips currently in progress
• Answering company policy questions from documentation
• How Foundry orchestrates multiple knowledge sources
Finally, we discuss how this type of agent can be embedded into custom applications, enabling enterprise systems to support natural language queries over both data and documentation.