⚡Give Your AI Agent Access to Microsoft Fabric Lakehouse Data
Details
Most people hand an AI agent a spreadsheet and hope it reads it right, like pasting a CSV into ChatGPT. In real companies, agents need clean, repeatable data. Microsoft Fabric Pipelines, Dataflows, and Lakehouses prepare that data first, so the agent isn't guessing on messy rows or waiting on someone to re-upload files. You build the pattern once and reuse it for sales, finance, or ops agents.
⚡Map the Fabric to AI Agent Architecture
Learn to trace how raw business data moves from a CSV through Pipeline, Dataflow, and Lakehouse to an AI agent.
⚡Clean Raw Sales Data With Dataflow Gen2
Learn to remove null rows, fix data types, and calculate revenue in Dataflow Gen2 on a free Fabric trial.
⚡Land Trusted Data in a Fabric Lakehouse
Learn to write cleaned data to a Lakehouse Delta table in OneLake that your agent can query reliably.
⚡Orchestrate the Flow With a Data Pipeline
Learn to use a Pipeline to run ingestion and your Dataflow in order, so data refreshes without manual steps.
⚡Connect an AI Agent to Your Fabric Data
Learn to give a Python LangGraph agent a query tool so it can answer questions like which store lost the most revenue.