Fabric Data Agent Fabric Data Agent
Your community hub for Microsoft Fabric Data Agent resources
Discover articles, videos, tools, events, and resources - all curated by the community.
What is a Fabric Data Agent?
New here? Start with our Beginner Learning Path
A guided path from zero to building your first Fabric Data Agent - articles, videos, and hands-on resources in the right order.
Learning Paths
View all →Getting Started with Fabric Data Agents
Everything you need to know to get up and running with Microsoft Fabric Data Agents. Start here if you're new.
Build Your First Data Agent
A hands-on learning path that guides you through building a functional data agent from scratch.
Production-Ready Agent Patterns
Advanced patterns and architectures for deploying data agents at scale in production environments.
Articles
View all →May 15, 2026
Blog: What’s new in Neo4j Graph Intelligence for Microsoft Fabric - shows how to use exported results with Fabric Data Agents
Graph Intelligence for Fabric can now Export results to Lakehouse to answer complex data questions in Copilot using Fabric Data Agents — Whether you run PageRank or Betweenness to find influential products, the most likely bottlenecks in supply chains, or detect fraudulent communities with Louvain, these graph-based insights can be replicated back to the source tables or create fresh result tables.
May 13, 2026
Data Agent Now Supports Eventhouse Functions, Materialized Views, and Shortcuts (Preview)
New capabilities in Fabric Data Agent are now available to expand what's possible when data agent is integrated with Eventhouse KQL Databases. Data Agent now supports Eventhouse User Defined Functions (UDFs), Materialized Views (MV), and Shortcut tables enabling your AI-powered agent access to the full breadth of your Eventhouse KQL database.
Apr 27, 2026
Building a Fabric Data Agent with On‑Premises SQL Server Data
Many organizations still rely on on‑premises SQL Server for core operational workloads — finance, billing, ERP, manufacturing, and regulatory systems — that cannot be easily migrated to the cloud. At the same time, the business is demanding: Conversational access to data (“Ask the data” experiences) AI‑powered insights without building fragile ETL pipelines Faster time‑to‑value from analytics and AI Strong governance, security boundaries, and system ownership Microsoft Fabric addresses this challenge by allowing organizations to bring AI to their data before moving their data to AI. By leveraging SQL Server mirroring into Microsoft Fabric, organizations can continuously replicate on-premises data into OneLake, making it immediately available for analytics and AI—without disrupting source systems. On top of this foundation, semantic models and Fabric Data Agents enable governed, natural-language interactions powered by Microsoft Copilot.
Videos
View all →May 26, 2026
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!
May 15, 2026
Fabric Data Agents Series (4/4): Governance Guide - Can You Trust Your AI? 🔐
In Part 4 of our Microsoft Fabric Data Agents series, we focus on the most critical aspect of AI adoption: governance, security, and trust. This session shows how to design compliant, secure, and auditable Data Agent experiences so your AI solutions don’t just work — they work responsibly at scale. 🎯 What you’ll learn: ✔️ Execution identity — understanding who the agent acts as ✔️ Enforcing Row-Level Security (RLS) and Object-Level Security (OLS) ✔️ Building auditability and traceability into AI workflows ✔️ Preventing data oversharing and leakage ✔️ Applying governance best practices aligned with Microsoft Purview concepts
May 8, 2026
Fabric Data Agents Series (3/4): How to Use Fabric Data Agents in Real Apps #vfpug
Understanding Data Agents is one thing… using them in real applications is another. In Part 3 of our Microsoft Fabric Data Agents series, we focus on how to actually implement and consume Data Agents in production scenarios. This session breaks down the most effective architectural patterns to bring Data Agents to life across different audiences — from business users to developers and enterprise systems. 🎯 What you’ll learn: ✔️ How Copilot experiences work with Fabric Data Agents ✔️ Building custom business Q&A applications ✔️ Enabling agent-to-agent workflows using MCP ✔️ Designing enterprise APIs with GraphQL + agent translation ✔️ How to choose between Data Agent APIs, MCP, and GraphQL based on your use case and risk profile This is where strategy meets execution — helping you move from experimentation → scalable, real-world AI solutions.
Events
View all →Jun 25, 2026
Fabric Data Agents
Fabric data agents are now generally available in Microsoft Fabric, and they give teams a way to build conversational Q&A experiences over data sources like Power BI semantic models. But the real work is not just creating the agent. It is shaping the semantic model, instructions, examples, and feedback loop so the answers are robust. Which is why our next Wellington Power BI & Fabric User Group session is on Fabric data agents: what they are, where they fit, and how to get better results from them.
Jun 15, 2026
Microsoft Fabric Community Conference 2026
The annual Microsoft Fabric Community Conference featuring sessions on data agents, real-time analytics, and the latest Fabric innovations.
May 28, 2026
Less Puzzles, More Answers: Teach Your Data to Talk Back with Fabric Data Agents
Getting insights from your data shouldn’t feel like solving a puzzle. In this session, Jennifer will demo several Microsoft Fabric Data Agents so you can see them in action. This session is ideal for Power BI and Fabric practitioners who want to turn governed data into conversational, self‑service answers using real‑world AI patterns. No AI background required. Familiarity with Fabric or Power BI is helpful but not mandatory.
Resources
View all →Agentic App with Fabric (incl Data Agents)
Agentic Banking App is an interactive web application designed to simulate a modern banking dashboard. Its primary purpose is to serve as an educational tool, demonstrating: How SQL-based databases are leveraged across different types of workloads: OLTP, OLAP, AI workloads. How agile AI-driven analysis and insight discovery can be enabled via prescriptive data models in Fabric. How easy it is possible to integrate other Fabric workloads (e.g., Report, Data Agent, Notebook) leveraging that data model. Through a hands-on interface, users can see the practical difference between writing a new transaction to the database, running complex analytical queries on historical data, and using natural language to ask an agent to query the database for them.
Data Agent Governance and Security Accelerator Using Purview
Enable Microsoft Purview Data Security Posture Management (DSPM) for AI across Microsoft 365 Copilot, Microsoft Foundry, Microsoft Fabric, and custom AI solutions with a spec-driven deployment and governance workflow.
Data Agent Python SDK Notebooks
Notebooks to evaluate, automate, test data agents
Tools
View all →Data Agent Automation Using Python SDK
Notebook to automate creation, testing and updating data agent using Python SDK in Fabric notebook.
Data Agent Client - Teams App
A Microsoft Teams application that enables natural language queries to Fabric Data Agents, featuring real-time streaming responses, DAX query visualization, and multi-agent support. - Natural Language Queries: Ask questions in plain language and get AI-powered answers from your semantic models - Multi-Agent Support: Connect to multiple Fabric Data Agents and switch between them seamlessly - Real-Time Streaming: See responses as they're generated with live progress indicators - Query Transparency: View the generated DAX code and query results for each analysis step - Teams Integration: Native Teams app experience with SSO authentication - Fluent UI Design: Modern, responsive interface following Microsoft design guidelines
LinkedIn Posts
View all →Building a Fabric Data Agent with On‑Premises SQL Server Data
Many organizations still rely on on‑premises SQL Server for core operational workloads — finance, billing, ERP, manufacturing, and regulatory systems — that cannot be easily migrated to the cloud. At the same time, the business is demanding: Conversational access to data (“Ask the data” experiences) AI‑powered insights without building fragile ETL pipelines Faster time‑to‑value from analytics and AI Strong governance, security boundaries, and system ownership Microsoft Fabric addresses this challenge by allowing organizations to bring AI to their data before moving their data to AI. By leveraging SQL Server mirroring into Microsoft Fabric, organizations can continuously replicate on-premises data into OneLake, making it immediately available for analytics and AI—without disrupting source systems. On top of this foundation, semantic models and Fabric Data Agents enable governed, natural-language interactions powered by Microsoft Copilot.
Data Agent to Data Memo
Experimented with a pipeline to explore the data autonomously, find insights, find second order signals, create a cohesive narrative that's grounded in the data & context, added an optimization layer using DSPy (Community) to tune it and converted to speech. No external APIs, services, 100% in hashtag#MicrosoftFabric . 🔊Listen, let me know. The novelty here is I used the recently published LLM-as-Verifier approach as a metric in GEPA to tune the narrative. (https://lnkd.in/g2Huycw9)
𝗖𝗵𝗮𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 in Copilot, Data Agents or Databricks One? 🤖
Is your data AI ready? Are your "consumers" ready for it? Don't let AI solely write your instructions, documentation and verified answers. You need a documentation system and process to steer the process. See the guide for links to notebooks, scripts and skills that take you further! My new favorite Tabular Editor C# Macro is on slide 5 😍 I'm happy to see that if you want to succeed with Chat with your data, optimizing and documenting semantic models are still (if not more) important.
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