Fabric Data Agent #lakehouse
4 items tagged with "lakehouse"
🎬 Videos
Feb 10, 2026
Configuring Fabric Data Agent to Accelerate Data to Decisions
In this session, attendees will explore how to build and configure a Fabric Data Agent from the ground up. We’ll walk through practical commands and functions while working with data sourced from a Lakehouse, review data source setup and sample queries, and showcase how easily a Fabric Data Agent can be incorporated into a multi-agent workflow using Microsoft Foundry.
May 1, 2025
Microsoft Fabric Data Agents Explained
From query to conversation — demonstrates setting up a Fabric Data Agent, connecting it to a Lakehouse, customizing prompts, and using chat-based analytics to extract insights from your data in real time.
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Mar 16, 2026
How to build an AI agent in Fabric and Foundry to leverage your business data
If you have been waiting for a step‑by‑step tutorial to make an AI agent that “speaks” the language of your business data, this guide is for you. In this article I will show you how to: (1) build a Fabric Data Agent over your Fabric Lakehouse and (2) connect that agent to Microsoft Foundry to enable extended agent capabilities.
Mar 26, 2026
What We Learned Building a Real-World Fabric Data Agent — The Honest Field Notes
The post is a detailed, field‑tested account of building a real‑world Fabric Data Agent on top of a complex industrial supply‑chain dataset, highlighting what works, what breaks, and what requires deliberate engineering. It walks through six major lessons: why agent instructions—not data sources—determine answer quality; how Semantic Models and Ontologies complement each other; the pitfalls of asynchronous graph rebuilds; the importance of using binding property names in GQL; common GQL query‑generation failures; and the hidden requirement to use dbo. prefixes in SQL. Despite the rough edges of a preview product, the author argues that Fabric IQ’s architecture—unifying SQL, DAX, and GQL behind a governed semantic layer—is powerful and worth understanding deeply for anyone building enterprise‑grade AI analytics.