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.
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.
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.
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. |
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.
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