In this walkthrough, I configured Microsoft Fabric Data Agents to answer questions against business data using natural-language prompts. The focus was on selecting data sources, guiding agent behavior with instructions and example queries, testing responses, and publishing the completed agent.
You can watch the video in English with English subtitles. Please check the subtitle settings on YouTube.
A data agent can be useful when company data is distributed across different sources and users need more consistent answers to recurring questions, such as questions based on sales data.
Key Takeaways
- I added data sources such as Eventhouse, Semantic Models, and Lakehouses to a Fabric Data Agent.
- I selected the tables the agent could read and observed a consistency warning when selecting more tables.
- The agent translated natural-language questions into the query language appropriate for the connected source.
- I used data source interactions, example queries, and agent instructions to influence responses.
- I tested custom instructions before publishing the agent.
- I also showed a Power BI Copilot scenario where a Data Agent could be referenced without opening the agent directly.
What I Configured in Microsoft Fabric Data Agents
I started in a workspace with a Fabric license and opened a Data Agent I had previously created as an example. The interface shown at the beginning was the interface from an earlier video, before I later shared a short excerpt of updates.
The first important step was opening the Data Source option. From there, I could add an Eventhouse, Semantic Models, or a Lakehouse. In my example, I added four Lakehouses.
After adding the sources, I selected the tables that I wanted the Data Agent to read. When I selected more tables, the agent displayed a warning that results might not be consistent. This is an important configuration point: expanding the number of available tables can give the agent more data to work with, but the interface warns that consistency may be affected.
Once the sources were available, I could either start with sample questions or enter my own prompt. The purpose was to ask questions in natural language and receive an answer based on the selected business data.
How Microsoft Fabric Data Agents Translate Questions
One of the key behaviors I demonstrated was how Microsoft Fabric Data Agents convert a natural-language request into a query language that matches the connected data source.
For the sources discussed in the video, the agent uses different query approaches:
- Eventhouse: KQL
- Semantic Models: DAX
- Lakehouse or Warehouse: SQL
This means I could begin with a plain-language prompt while the Data Agent generated the source-specific query needed to retrieve a response. The exact query language depends on the type of data source attached to the agent.
Using Data Source Interactions and Example Queries
Next, I moved into the configuration areas that help shape how a Data Agent responds. In the Data Source Interactions section, I entered instructions related to a specific data source. This was where I could provide guidance on how the agent should work with that source.
I also used Example Queries, which are sample questions connected to a desired query behavior. My goal was to ensure that, when a particular question was asked, the specified SQL query would run consistently.
I compared this behavior to the Verified Answers concept for a Power BI visual. In this scenario, the sample question acts as a controlled way to steer the agent toward the expected query and output.
The available setup differs by source type:
- For a Lakehouse or Warehouse, SQL appears in this configuration flow.
- For an Eventhouse, the agent responds through KQL.
- For the Power BI side, I mentioned preparing the data for AI so that the Data Agent can automatically detect it.
This is where example queries can be useful for recurring business questions. Rather than relying only on an open-ended prompt, I can add known questions and define the behavior I want for them.
Testing Agent Instructions Before Publishing
In addition to source-level settings, I configured Agent Instructions. These are broader instructions that tell the agent how to approach its responses.
To test this behavior, I added a simple instruction asking the agent to always address me as “boss.” I then entered a sample prompt into the agent. In this test, the response included the requested word while also returning an answer based on the prompt.
I entered another prompt that matched one of the example queries I had written. The agent used that example-query path and returned the output in the same format as the sample query. I repeated this with another example to check whether the configured behavior appeared consistently in these tests.
This was a short functional check rather than a comprehensive evaluation. I tested the instruction and example-query behavior shown in the agent interface, but I did not cover broader accuracy measurement, performance testing, or governance controls.
Publishing a Microsoft Fabric Data Agent
After I was satisfied with the agent, I selected Publish. Before publishing, I could add a description to help explain what the Data Agent was for.
Once published, the interface displayed the implementation and output for the agent. Publishing is the point where the configured Data Agent becomes ready to use beyond the editing and test flow I demonstrated.
I also included a short September 2026 excerpt showing an updated agent experience. In that excerpt, I showed options to view existing data sources through Add Data, add tools, and work with visualizations. I also mentioned a capability that can help generate instructions more quickly, rather than manually writing every example query, data-source instruction, and agent instruction.
The updated experience also included a Test Data Agent area for checking the agent before publishing. I referenced Runtime as part of working with the newer updates shown in the interface.
Using a Data Agent from Standalone Power BI Copilot
Finally, I showed a scenario involving Standalone Power BI Copilot. The Data Agent was offline at the time of the recording, but I included the previously available scenario to demonstrate the intended interaction.
In this flow, I used the @ symbol to reference one of my Data Agents. I had created an agent named Copilot Data Agent. The goal was to ask a question in Standalone Power BI Copilot and receive an answer from the Data Agent without first navigating directly into the agent itself.
This illustrates how Microsoft Fabric Data Agents can be part of a broader Copilot-based experience, where users ask questions from a conversational interface while the agent works with its configured data sources.
Frequently Asked Questions
Which data sources can I add to a Microsoft Fabric Data Agent?
In the demonstrated interface, I could add an Eventhouse, Semantic Models, and Lakehouses. I then selected the tables that the agent could read.
Which languages can a Fabric Data Agent use for queries?
The video showed KQL for Eventhouse, DAX for Semantic Models, and SQL for Lakehouse or Warehouse sources.
Conclusion
In this walkthrough, I configured Microsoft Fabric Data Agents by connecting data sources, selecting tables, adding source instructions, defining example queries, and testing agent instructions. I then published the agent and showed how it could be referenced from Standalone Power BI Copilot.
The main takeaway is that Microsoft Fabric Data Agents provide a configuration-based way to connect natural-language questions with business data sources and source-specific query languages. If you are working with Fabric data sources, start by defining a focused set of tables and recurring questions, then test the responses before publishing.
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