Fabric Data Agent Parv Chana
14 contributions
đź“„ Articles
Jan 1, 2026
Chat with your data using Microsoft Fabric data agents
Chat with your data using Microsoft Fabric data agents A Microsoft Fabric data agent enables natural interaction with your data by allowing you to ask questions in plain English and receive structured, human-readable responses. By eliminating the need to understand query languages like SQL (Structured Query Language), DAX (Data Analysis Expressions), or KQL (Kusto Query Language), the data agent makes data insights accessible across the organization, regardless of technical skill level.
Jan 9, 2026
Creating Data Agent in Microsoft Fabric — First Impression
To give you a bit of background on what this data agent is all about, think of it as your assistant that knows everything about your data. Just like you use ChatGPT, you can leverage LLM capabilities on your enterprise data and have conversations over your database.
Jan 1, 2026
Data Agent in Microsoft Fabric – Here’s How it Works by Azure Ops
Data Agent in Microsoft Fabric – Here’s How it Works Ever wished you could just ask your data questions in plain English and get instant, intelligent answers? With Microsoft Fabric’s new Data Agent, that’s not just possible, it’s powerful. In this post, I’ll walk you through how I built a Fabric Data Agent on top of the standard AdventureWorksDW dataset, and how you can too, even if you’re a complete beginner.
Mar 26, 2026
Fabric Data Agents: The Shift from Querying Data to Reasoning Over Knowledge
What’s the big deal? Fabric is no longer just a data platform. It’s becoming a system that can reason over enterprise knowledge. The real shift is the move from human-authored to AI-authored logic, under guardrails. Before Humans write explicit queries, design semantic models, orchestrate pipelines. Tools act as passive executors, doing exactly what they are told. Now Humans declare the intent. Agents determine the plan (how to answer) and execute across governed data, semantic and orchestration layers.
Mar 31, 2025
Implementing Data Agent in Microsoft Fabric for comprehensive business insights
Leveraging the semantic model for business insights The semantic model in Microsoft Fabric plays a crucial role in transforming raw data into meaningful business insights. By accessing the semantic model, Data Agent can understand and interpret complex data relationships, providing a unified view of the data that aligns perfectly with current reporting and analytical needs.
Aug 12, 2025
Microsoft Fabric Data Agent — Complete Guide by Alpa Buddhabhatti
Unlock natural language access to your enterprise data with Microsoft Fabric Data Agent.
Apr 2, 2026
Programmatically Retrieve Prep Data For AI Configuration of Semantic Models
For Power BI Copilot and Data agents with semantic models, you must use Prep Data for AI configuration to ground the responses in the context added in Prep for AI. In this blog, I will show you how you can use the Power BI remote MCP server to get the configuration.
🎬 Videos
Apr 18, 2026
Best Practices for Data Agents in Microsoft Fabric
A deep dive into building and leveraging Data Agents within Microsoft Fabric — AI-powered autonomous systems that integrate with Fabric's lakehouse, warehouses, and semantic models to intelligently query, transform, and analyze data using natural language. This session will explore how Fabric's agent capabilities streamline data workflows and how to configure data agents with the right context to improve accuracy and reliability.
Apr 16, 2026
Fabric Data Agents Series (1/4): From Queries to AI Reasoning in Microsoft Fabric
This session kicks off our 4-part series on Microsoft Fabric Data Agents, exploring a major shift in how we interact with data — from writing queries to AI-driven reasoning under governance. In Part 1, we break down what Fabric Data Agents are (and are not), how they fit into the Microsoft Fabric ecosystem, and how the agent reasoning loop plans and executes grounded, trustworthy answers across governed data. You’ll learn why this shift matters, how it impacts Power BI developers and data teams, and what it takes to move from prompt-level experimentation to system-level AI thinking. 🚀 This is your starting point to understanding how semantic models, architecture, and governance come together to enable reliable, explainable, and scalable AI-powered analytics. 👉 Stay tuned for Parts 2–4, where we dive deeper into design patterns, architecture, and operating Data Agents at scale. 👍 Like, comment, and subscribe for more deep dives on Microsoft Fabric, Power BI, and AI in data. #MicrosoftFabric #PowerBI #FabricDataAgents #AIinAnalytics #DataPlatform
Apr 26, 2026
Fabric Data Agents Series (2/4): Why Your Data Model Breaks AI in Microsoft Fabric
👉 If your data model isn’t solid, your AI won’t be either. In Part 2 of our Microsoft Fabric Data Agents series, we focus on the foundation that makes or breaks AI: your semantic model and data design. This session shows how to build AI-ready data foundations so Fabric Data Agents can deliver accurate, consistent, and trustworthy results — not hallucinations or ambiguity. 🎯 What you’ll learn: ✔️ Why naming conventions and clear definitions are critical for AI reasoning ✔️ How explicit measures improve consistency and trust ✔️ The role of star schema design in scalable AI analytics ✔️ Common failure modes that break Data Agents ✔️ Practical Fabric-native fixes to improve grounding quality Most AI projects don’t fail because of AI — they fail because of bad data modeling. This session gives you the frameworks to fix that. 🚀 Whether you're a Power BI developer, data engineer, or architect, this is your blueprint for moving from experiments → production-ready AI systems. 👉 Watch Part 1 to understand Fabric Data Agents fundamentals 👉 Continue with Parts 3 & 4 to master architecture and governance at scale 👍 Subscribe for more deep dives on Microsoft Fabric, Power BI, and AI-driven data platforms #MicrosoftFabric #PowerBI #DataModeling #FabricDataAgents #AIinAnalytics
Apr 23, 2026
Microsoft Fabric: How to use Data Agent Example Queries
Example Queries are THE key value differentiator between Microsoft Fabric Data Agents and all other RAG agents you can build, so let's dive into this topic with Bradley Ball, aka ‪@SQLBalls‬ , and look and how we can use them!
đź“… Events
Apr 23, 2026
Designing AI‑Ready Data Surfaces: Why Agents Expose Your Weak Spots
Part 2 or 4: Build the semantic and data foundations that make agents reliable. This workshop translates modeling best practices into agent-ready guidance: naming and definitions, explicit measures, star schema discipline, and how to prevent ambiguous data surfaces. Includes common failure modes and Fabric-native remediation strategies to improve grounding quality.
Apr 16, 2026
Designing Trusted Fabric Data Agents - Part 1
This four‑part series guides attendees through the intricacies of Microsoft Fabric Data Agents - from understanding how agents reason over data, to designing AI‑ready data foundations, selecting the right consumption architectures, and operating agents safely at scale. Participants will learn how to move beyond prompt‑level experimentation to system‑level thinking, how agent reasoning, semantic modeling, architectural patterns, and governance controls work together to produce reliable, explainable, and compliant AI‑driven outcomes. Each session builds on the last, equipping teams with practical frameworks, patterns, and checklists to confidently deploy Fabric Data Agents across diverse audiences while managing risk, trust, and performance. Part 1 or 4: A guided tour of why Fabric Data Agents represent a shift from human-authored queries to AI-authored execution logic under guardrails. Learn what a data agent is (and is not), how it fits into the Fabric ecosystem, and how the agent reasoning loop plans and executes grounded answers across governed Fabric artifacts.
May 7, 2026
Part 3 / 4: Fabric Data Agent Architecture Patterns: Choosing the Right Consumption Path
Part 3 or 4: Explore the most common and successful architectural patterns for consuming Fabric Data Agents: native Copilot experiences, custom business Q&A apps, agent-to-agent developer workflows via MCP, and enterprise data APIs with optional agent translation. Learn how to choose between Data Agent API, MCP server, and GraphQL depending on your consumers and risk profile.