Fabric Data Agent Articles
Curated articles about Microsoft Fabric Data Agents, handpicked by the community
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.
Apr 25, 2026
The Agents: Data Agent & Operations Agent in Practice
A solutions architect's deep dive into the two agents powering Fabric IQ - Data Agents and Operations Agents. Covers the execution chain, governance model, billing math, configuration levers, and the combined architecture where one agent detects and the other explains over a shared Ontology.
Apr 7, 2026
New article: Which Verdicts Changed, and Why: a Row-Level Audit of Fabric Data Agent Evaluation
The author performs a detailed row‑level audit of a 72‑question benchmark to understand why evaluation verdicts changed after fixing errors in the benchmark itself. Many initial “failures” turn out to be caused by faulty ground truth, ambiguous phrasing, or inconsistent casing rules rather than true Data Agent mistakes. After refining benchmark wording, tightening Agent instructions, and clarifying metric definitions, accuracy rises to 97.2%. The few remaining errors stem from extremely complex multi‑step prompts and ambiguous schema references, revealing limits of the underlying model rather than flaws in the benchmark.
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.
Apr 1, 2026
Designing AI‑Ready Analytics with Microsoft Fabric Data Agents
s organizations accelerate their adoption of AI-powered analytics, a new challenge is emerging: how to expose trusted business data to AI—Copilot, data agents, and conversational analytics—in a way that is secure, cost-predictable, and architecturally sound. Most enterprises are already operating in a complex reality: Power BI reports connected to operational systems like Oracle, SAP, and SQL Server A mix of Import and DirectQuery semantic models Strong governance expectations around security, lineage, and cost control
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 17, 2026
We Built the Benchmark. Now Let’s Evaluate the Fabric Data Agent for Real
This article shows how to move from a benchmark design to a real evaluation workflow for a Microsoft Fabric Data Agent. Starting from a 72-question benchmark built in a previous article for an Italian multilingual scenario, it explains how to complete the ground-truth dataset, run evaluate_data_agent on Fabric, inspect summary and row-level results, and use notebooks to operationalize the full process. A key insight is that part of the observed weakness may come not only from the Data Agent, but also from the evaluation layer itself. By inspecting the SDK source code and testing a stricter custom critic prompt, the article shows how evaluation reliability can improve significantly without changing the agent or the benchmark. Overall, the piece is a practical guide to benchmarking and evaluating Fabric Data Agents more rigorously, especially in multilingual business scenarios.
Mar 14, 2026
Building a Spider2-Inspired Benchmark to Measure the Real Robustness of a Fabric Data Agent in Italian
This article moves from working demos to measurable reliability by introducing a Spider2-inspired benchmark for evaluating a Fabric Data Agent in Italian. It explains why manual spot checks are not enough, and shows how to design a more rigorous evaluation framework that separates already-taught patterns from true generalization. The result is a practical benchmark design for assessing multilingual Fabric Data Agents beyond isolated successful examples.
Mar 4, 2026
Fabric Data Agents Are English-First (For Now): A Hands-On Guide to Configuring One on Zava DIY for Non-English Users
This article provides a hands-on, incremental guide to configuring a Microsoft Fabric Data Agent on the Zava DIY dataset for non-English users, while keeping the agent grounded in an English-first setup. It shows how to improve reliability step by step through data source descriptions, agent instructions, domain constraints, formatting rules, and validated example queries, then extends the configuration with a practical "translate in, translate out" approach. The result is a reproducible quick-win pattern for making the agent more analytics-ready across languages without introducing external translation layers or custom front ends.
Jan 28, 2026
Microsoft Fabric Copilot: Building a Foundation of Trust Before You Ask Questions!
The blog discusses why establishing a strong foundation of trust is essential before users begin asking questions to Microsoft Fabric Copilot. It emphasizes that Copilot’s effectiveness depends heavily on the quality, clarity, and governance of the underlying data and semantic models. The author explains how organizations should prepare their Fabric environment—through well‑modeled data, proper documentation, security, and metadata—so Copilot can generate accurate, reliable, and context‑aware responses. Overall, it’s a call to treat Copilot not as magic, but as a powerful layer built on top of disciplined data practices.
Jan 19, 2026
Creating Microsoft Fabric Data Agent using python sdk programmatically
This article explores creation of Data Agent programatically using Python SDK for data agents.
Jan 13, 2026
Analyzing Microsoft Fabric's Data Agent
This article analyzes data agents on a variety of tasks and queries and performs some prompt engineering to find out the change in responses.
Jan 12, 2026
Creating Data Agent in Microsoft Fabric - First Impression
The article walks us through the creation of data agent for first time user and explores various ways to play around data agent.
Jan 10, 2026
Best Practices for Fabric Data Agent Development
A collection of best practices and design patterns for building production-ready data agents in Microsoft Fabric, including error handling, performance optimization, and security considerations.
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 2, 2026
Using Microsoft Fabric Data Agent in Non-English Languages: A Practical Exploration
This article examines what Microsoft Fabric Data Agent's current non-English limitation means in practice, using Italian as a concrete business scenario. Rather than stopping at the official "English-first" guidance, it presents three pragmatic patterns for enabling multilingual experiences today: English instructions with translate-in/translate-out behavior, Copilot Studio as a multilingual front-end, and a translation gateway built around the Data Agent API. The goal is to help teams choose the right architecture for multilingual adoption without overestimating native language support.
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 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.
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.
Jun 4, 2025
Automatically populate Data Agents with Semantic Model Synonyms
Automatically populate Data Agents with Semantic Model Synonyms, however note that synonyms are used by data agents. Excellent blog none the less on getting the data AI ready.
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.
Mar 26, 2025
What We Learned Building a Real-World Fabric Data Agent
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.