Table of Contents
Introduction
Data is everywhere in modern organizations, but access and usability are still common bottlenecks. Teams often spend more time locating, preparing, and validating data than actually using it to answer business questions.
This challenge is growing as analytics environments scale. In Microsoft Fabric alone, the amount of data stored in OneLake has increased by more than six times over the past year, reflecting the rapid expansion of enterprise data volumes. The issue is no longer data availability; it is enabling fast, reliable interaction with that data across systems without adding operational complexity.
Microsoft Fabric Data Agents address this gap by serving as an intelligent layer between users and their data, translating user queries into structured queries and returning validated results.
In this blog, we will discuss what data agents are, how Microsoft Fabric Data Agents work, their prerequisites and limitations, how they differ from Fabric Copilot, and where they fit in a modern analytics workflow.
What Are Data Agents?
Data agents are applications powered by artificial intelligence (AI) that remove traditional barriers to analytics, allowing users to interact with data in plain English (Natural Language Querying) rather than writing complex queries/code. This ease of use enables more users to draw insights from the underlying data, resulting in better decision-making.
The data agents are designed to automate and simplify various data management activities. The serviceability of the data agent may vary significantly according to the systems and requirements of the organization.
Microsoft Fabric Data Agents
Microsoft Fabric Data Agents is a new feature in Microsoft Fabric, powered by AI applications (LLMs). It makes data insights more accessible and enables users to interact with enterprise-scale data using natural language querying to gain insights, without writing a single line of code for SQL, DAX, or KQL queries.
You can connect up to 5 data sources with the Data agent in any combination, which can be Lakehouse, Warehouse, Semantic Mode, or KQL Database, and only makes read-only data collection to the selected data sources.
The data agent in Fabric respects the restrictions enforced by the organization, and as a result, users can only receive insights for the data they are authorized to see.
Prerequisites:
- A Paid Fabric Capacity (P2 or higher)
- Fabric Data agent tenant-level setting is enabled.
- Cross-geo data processing and storing AI are enabled.
- Power BI semantic models via XML endpoints switch are enabled.
- At least one structured data source with table-level data — a lakehouse, warehouse, Power BI semantic model, KQL database, or ontology. (Raw files such as CSV or JSON are not supported unless ingested as tables.)
Limitations:
This Microsoft Data Agent is in public preview and has certain limitations, as outlined below.
- Only supports the read-only queries in SQL, KQL, and DAX.
- Data agents don’t work with unstructured data sources.
- Only support queries in the English language.
- The LLM Model underlying cannot be changed by the Data agent.
- Response latency is typically 10–30 seconds per query — suitable for interactive Q&A but not real-time operational monitoring.
- No visualization output — responses are text-based. Chart or graph generation is not built into the agent response.
- Cross-region configurations are not supported. The data agent workspace and the data source workspace must share the same Fabric capacity region.
Bonus Reading: Microsoft Fabric’s ROI: Cost-Saving Features and Benefits
Build Microsoft Fabric Solutions Around Your Data
Design and implement Fabric solutions aligned with your data sources, reporting needs, and analytics workflows, including Data Agents and advanced use cases.
Build My Custom Fabric SolutionHow Does the Microsoft Fabric Data Agent Work?
When a user submits a query to the Fabric Data agent, a sequence of background activities occurs to provide a response.
Step 1: Query Parsing
The agent responsible for interpreting user questions is Fabric Data, which utilizes Azure OpenAI. This process verifies the interpretation of the questions, subject to security measures and permissions.
Step 2: Identification of Data Source.
Once the question has been comprehended, the system compares it with all the data sources configured for the Fabric Data agent. To access the data, the system requires the user’s login information, which limits access to information visible to the user.
Step 3: Query generation
Once the right data source has been chosen, we ensure that the appropriate tool is used to produce a structured query that will be executed on the data. It is NL2SQL in the case of Relational Databases. In the case of Power BI semantic models, it uses NL2DAX. In the case of KQL databases, it employs NL2KQL.
Step 4: Query Validation
In this process, validation is done to ensure that the query is correctly constructed and is compliant with the security measures.
Step 5: Response
The query is executed against the chosen data source, and the output is structured in a human-readable format, which is then distributed to the user.
Real Business Use Cases: Where Organizations Are Using Fabric Data Agents
Fabric Data Agents are not a future concept — organizations are deploying them across core business functions. Here are four concrete examples.
1. Finance: Budget and Variance Analysis on Demand
A financial controller asks: “Which departments exceeded their Q1 budget by more than 15%, and what are the top three expense categories driving the overage?” With a data agent configured on the company’s warehouse and Power BI semantic model, the controller gets a precise, permission-scoped answer in under 30 seconds — and can ask follow-up questions in the same conversation. No BI team request needed.
2. Retail: Inventory and Supply Chain Intelligence
A regional supply chain manager queries: “Which SKUs have fallen below safety stock levels in the North-East region, and what is the average lead time from our top three suppliers for those items?” The agent joins data across a lakehouse (inventory) and a KQL database (supplier event logs) in a single response — work that previously required manual pulls across multiple tools.
3. Healthcare / Public Sector: Compliance Reporting Without IT Dependency
A compliance officer asks: “How many patient records were accessed by external contractors in the last 90 days, and does that access align with documented consent records?” With the agent configured on their governance lakehouse and existing access controls enforced, the officer runs ad-hoc compliance checks without involving IT — while the audit trail is maintained automatically.
4. HR and Operations: Workforce Analytics for Leadership
A COO asks: “What is the average time-to-hire across business units this quarter, and which recruiting source has the highest 90-day retention rate?” Rather than waiting for a scheduled report, the COO gets an instant governed answer from the HR semantic model — and can explore follow-up questions in real time before a board meeting.
How Microsoft Fabric Data Agent is different from Fabric Copilot
Both the data agent and copilot utilize generative AI to process and share insights over the data; however, there are some key differences in their functionality and use cases that help in deciding between them.
Here is the breakdown of the key differences:
| Functionality | Fabric Copilot | Data Agent |
|---|---|---|
|
Configuration/Customization |
Pre-configured doesn’t allow customization |
Can be configured using custom interactions and examples |
|
Use case |
Embed in Fabric` |
Standalone application for Q&A, offering independent operation. |
|
Integration |
Limited to use within the Fabric ecosystem. |
Can be integrated with Copilot Studio, Teams, and AI Foundry. |
|
Data Sources |
Active user context |
Offers flexibility, as multiple data sources can be selected. |
|
Purpose |
Focus on assistance with the specific task |
Organization-wide availability |
|
Prompt Engineering |
Does not support the creation of custom prompts. |
Users can design and adjust prompts to meet specific requirements and scenarios. |
Expanded Decision Guide:
| When to use a Fabric Data Agent | When to use Fabric Copilot |
|---|---|
|
You want non-technical business users to query organizational data in plain English |
You want AI help writing SQL, KQL, or DAX queries inside a Fabric workload |
|
You need a domain-specific analyst available across the organization — Finance, HR, Operations, Compliance |
You are a developer or analyst working inside Notebooks, Dataflows, or Warehouses |
|
You want to integrate natural language data access into Microsoft Teams or M365 Copilot |
You need contextual suggestions within your current Fabric item — not a standalone Q&A tool |
|
You need to connect multiple data sources (up to 5) in one experience |
Your task is scoped to a single active item and does not need cross-source querying |
Do not use a Fabric Data Agent when:
You need real-time operational monitoring (latency is 10–30s). You require predictive analytics, ML inference, or causal analysis. Your queries are in a language other than English. Your data lives in raw files (CSV/JSON) not yet exposed as Fabric tables.
Do not use Fabric Copilot when:
You want business users — not developers — to self-serve data queries. You need a shareable, published experience that works across your organization. You need multi-source querying beyond the current Fabric item.
Bonus Reading: Microsoft Fabric Services: Is It Worth It?
Stabilize and Support Your Fabric Environment
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Get Fabric SupportConclusion
Microsoft Fabric Data Agent is a tool designed and developed to enhance interaction with multiple data sources available in OneLake. Its integration with multiple services, including Copilot Studio, Microsoft Teams, and AI Foundry, makes it a go-to solution for organizations to streamline their data flows and improve decision-making.
With the option for customization and configurability, the Microsoft data agent is a tool for organizations that want to leverage the data potential.
FAQs
They reduce the effort required to query and understand data by allowing users to ask questions in plain English, rather than writing SQL, DAX, or KQL.
Fabric Copilot is embedded within Fabric experiences and offers limited customization options, whereas Data Agents operate independently, supporting broader integration and configuration options.
No. Fabric Data Agents are strictly read-only and can only retrieve data from connected sources.
A single data agent can connect up to five data sources, including Lakehouse, Warehouse, Power BI semantic models, and KQL databases.
The agent adheres to existing Fabric and Power BI security protocols. Users can only view data for which they are already authorized to access.
No. A paid Fabric capacity (P2 or higher) is required, and specific tenant-level settings must be enabled.
No. At present, Data Agents only support structured data sources.
Readiness depends on three factors: (1) Capacity tier — you need F2 or P1 minimum. (2) Data foundation — you need structured, table-level data in Fabric. Raw CSV or JSON files will not work unless ingested as tables. (3) Governance — your semantic models or lakehouse tables should have clear, well-documented schemas and consistent naming conventions. Organizations that have already invested in a governed Power BI semantic model are typically best positioned to see immediate value.
Accuracy depends heavily on the quality of your data model and how well the agent is configured. Providing detailed Data Agent Instructions, curated example queries, and well-labelled table and column names significantly improves response quality. The agent can also be iteratively refined — you can review generated queries and add examples to address patterns where it underperforms. It is not a set-and-forget tool; it improves with tuning.
Yes. As of the Ignite 2025 announcements, Fabric Data Agents can be integrated directly into Microsoft 365 Copilot — making them accessible from Teams, the web app, or the desktop Copilot experience. Users can interact with the agent from within their existing workflow without switching to the Fabric portal. Sharing is governed by the same Fabric workspace permissions, so access controls remain intact.
No. Microsoft Fabric handles the Azure OpenAI integration for you — a Microsoft-managed Azure OpenAI Assistant is used under the hood. You do not need to provision, configure, or pay for a separate Azure OpenAI resource. Authentication is handled through your existing Microsoft Entra ID identity. Data access runs under the end user’s credentials, not a shared service account.
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