Fabric Data Agent LinkedIn Posts
Community-shared LinkedIn posts about Microsoft Fabric Data Agents
Building a Fabric Data Agent with On‑Premises SQL Server Data
View on LinkedIn →Building a Fabric Data Agent with On‑Premises SQL Server Data
Pablo Junco Boquer
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
Sandeep Pawar
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)](https://www.linkedin.com/company/dspy/) to tune it and converted to speech. No external APIs, services, 100% in [hashtag#MicrosoftFabric](https://www.linkedin.com/search/results/all/?keywords=%23microsoftfabric&origin=HASH_TAG_FROM_FEED) . 🔊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? 🤖
David Kofod Hanna
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.
Fabric Ontology with Data Agent
Chris Chalmers
AI built my Fabric Ontology demo in under an hour! Thanks to GitHub Copilot, I have notebooks for loading Lakehouse tables with demo data, building a Fabric Ontology, and creating instructions for a Fabric Data Agent to query the whole thing. Visit my GitHub repo to see the prompts I used, how Ontology Playground helped, and the skills files that were created along the way. 
What We Learned Building a Real-World Fabric Data Agent — The Honest Field Notes
View on LinkedIn →What We Learned Building a Real-World Fabric Data Agent — The Honest Field Notes
Ankit Kumar
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.
Conversational Analytics using Microsoft Fabric Data Agents(e2e tutorial)
Harsha Guggilla
This post explains how Microsoft Fabric Data Agents can help teams answer unexpected business questions in real time by turning natural business language into trusted, governed analytical answers. It highlights the common pressure data teams face in review meetings when metrics suddenly shift and shows how conversational analytics can work effectively only when built on approved data, definitions and guardrails.
Copilot vs Data Agents
Nadim Aboud-Khalil
Microsoft Fabric now gives us Data Agents - purpose-built, grounded on your actual data sources (Lakehouse, Warehouse, SQL DB, KQL DB), and fully customizable with AI instructions, example SQL queries, and tone/format control. Compare that to the standalone Copilot: broad access, but limited customization and no instruction support.
Data agent with SQL DB in Fabric
Sukhwant Kaur
🚀 You launched your first SQL database in Fabric? Wondering what else you can do next? Imagine being able to chat with your data—or empowering your whole team to interact with your database using natural language. That’s where Data Agents in Microsoft Fabric come in. Internet is buzzing with folks building Data Agents – Here’s your chance to to be part of the excitement. 👇 Check out my quick video to see what’s possible when you let Data Agents do the heavy lifting. Join the conversation, share what you build next, and let’s inspire each other to keep innovating! [hashtag#DataAgents](https://www.linkedin.com/search/results/all/?keywords=%23dataagents&origin=HASH_TAG_FROM_FEED) [hashtag#MicrosoftFabric](https://www.linkedin.com/search/results/all/?keywords=%23microsoftfabric&origin=HASH_TAG_FROM_FEED) [hashtag#LowCode](https://www.linkedin.com/search/results/all/?keywords=%23lowcode&origin=HASH_TAG_FROM_FEED) [hashtag#NoCode](https://www.linkedin.com/search/results/all/?keywords=%23nocode&origin=HASH_TAG_FROM_FEED) [hashtag#Innovation](https://www.linkedin.com/search/results/all/?keywords=%23innovation&origin=HASH_TAG_FROM_FEED) [hashtag#SQLdatabaseinFabric](https://www.linkedin.com/search/results/all/?keywords=%23sqldatabaseinfabric&origin=HASH_TAG_FROM_FEED) https://lnkd.in/eYwtMrNN
Data Agents with Business Central Data
Bert Verbeek
Ever wished you could ask natural language questions about your Business Central data and get instant insights? With the new Data Agent inside Microsoft Fabric, that’s now possible! In my latest blog, I dive into: ✅ How Data Agent connects to Business Central ✅ Why this is a game-changer for self-service analytics ✅ Practical examples of asking questions on your ERP data If you’re working with Microsoft Fabric and Business Central, this is a must-read!
From Power BI to Conversational AI: Building Data Agents That Scales
Khaled Chowdhury
Khaled will be presenting at SQL Saturday ATL on data agents
How to build an AI agent in Fabric and Foundry to leverage your business data
Arash Besadi
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.
Multi-agent routing for Fabric Data Agents using Microsoft Foundry
Mathias Halkjær
Creating multi-agent architecture with data agent and Microsoft Foundry
Production-Ready Microsoft Fabric Data Agents: A Practical Evaluation Workflow
Harsha Guggilla
This post explains why evaluating Microsoft Fabric Data Agents is essential before trusting them in production. It highlights that while these agents can reduce response time and ease pressure on data teams, their real value depends on whether their answers are accurate, consistent, and grounded in approved business logic.
From API to AI Agent: Turning Raw Data into Conversational Intelligence with Microsoft Fabric
View on LinkedIn →From API to AI Agent: Turning Raw Data into Conversational Intelligence with Microsoft Fabric
Mohammad Al-Qinneh
Data agent on top of Oura health data to chat with your own health data