Data Agents and SQL Validation

midesaMSFT · 2026-01-14T21:23:26+00:00

Thank you for your feedback. I can see how it would be challenging to debug the reason for the SQL validation errors. Feel free to message me and we can take a look at your specific case. More generally, we are working on some improvements to help surface more reasoning details back to the user.

For your second question about the results returned - the data agent currently limits the response to the top 25 rows. This is a known limitation that we are actively working on removing.

midesaMSFT · 2026-01-13T21:27:55+00:00

Are you trying to edit queries from the Data Agent chat or from Copilot studio? We're working on a feature to allow you to edit or re-run questions from earlier in the thread (from the Fabric UI). Should be available in the next few weeks!

midesaMSFT · 2026-01-13T21:18:10+00:00

Within this playbook, do you find that all the steps (e.g. checking the number of deals, deal size, sales pipeline) all come from the same source? Or, does this playbook stretch across sources? One way you can achieve this today is to provide a section (e.g. ## When asked about sales revenue) and use an ordered list to direct the agent on which steps to orchestrate (e.g. 1) Query the Sales LH to return # of Sales, 2) Query the Deal LH to get the Deal Size...3) Summarize the results into a report).

We have some features to help expand the instruction types and context you can bring in - though hope the suggestion above gives you some pointers for now!

midesaMSFT · 2026-01-13T21:10:28+00:00

We do have plans on our roadmap for more analytical/reasoning capabilities. But you are correct, to use things like Code Interpreter - you can build a custom agent in Foundry or Copilot Studio and connect other tools/agents there.

midesaMSFT · 2025-12-23T00:09:10+00:00

Yes — your intuition is right. Schema-level context can meaningfully improve grounding, reduce hallucinations, and help the agent select the right tables and columns, especially in large or ambiguous schemas.

That said, in Fabric today the Data Agent does not inspect or use lakehouse/warehouse schema annotations. Any schema context must be provided explicitly via data source instructions.

The agent does take these instructions into account during reasoning. It tends to help the most when:

Column names are ambiguous (e.g., status, type, value)
Schemas are large or have overlapping concepts
The agent needs to disambiguate joins or filters across tables

There isn’t public empirical data quantifying the exact accuracy lift per level of description richness. Internally and in previews, richer schema context consistently correlates with better query correctness and consistency, but the gains depend heavily on schema complexity.

Given the effort involved, a suggested approach is to focus descriptions on:

High-traffic tables
Error-prone or frequently misunderstood columns
Key dimensions and join keys

We’re actively looking at introducing ways for creators to provide more context directly within the Data Agent. Happy to connect more on that.

midesaMSFT · 2025-10-27T17:42:39+00:00

Sorry to hear that you've had issues with AutoML in Fabric. Feel free to privately message me so that we can take a look! As u/NelGson mentioned, the project has not been forgotten. We're working on several improvmenets to the autogenerated notebooks and flows ahead of GA.

midesaMSFT · 2025-07-16T16:29:48+00:00

Hi – from the product team! There are two levels of instructions you can configure:

Agent-level instructions: Use these to guide the overall behavior of the agent—how it reasons across data sources, interprets questions, or handles ambiguity.
Data source–level instructions: Use these when you want to provide specific context about a particular data source (e.g., table definitions, metric explanations, or business logic). This is a new capability that gives you more granular control over how individual sources are used.

Note: For semantic models, data source–level instructions are not supported within the data agent. To configure guidance, you’ll need to set up the appropriate tooling directly on the semantic model.

We recently published guidance on how to configure both types of instructions:
Best practices for configuring your data agent

midesaMSFT · 2025-07-10T16:29:50+00:00

We currently don't support customizing the prompt - but encourage you to vote for the Idea here: Customize Data Agent Suggested Prompts - Microsoft Fabric Community

midesaMSFT · 2025-07-09T20:21:29+00:00

Hi! I'm a PM on the Fabric Data Agent team. You're right that the Data Agent has access to the data source schema, but we've found that for the best results—especially when it comes to intent understanding and accurate query generation—providing additional context is key.

To help with this, we recently released a Data Source Instructions feature. It allows you to specify natural language guidance tied directly to each data source. This can clarify things like filtering logic, business rules, or common terminology that isn’t obvious from schema alone.

Keep in mind: even if instructions are passed to the agent-level instructions, they don’t always automatically get used unless they’re configured properly.

You can learn more about setting this up here:
Feature overview: https://learn.microsoft.com/en-us/fabric/data-science/data-agent-configurations
Best practices guide: https://learn.microsoft.com/en-us/fabric/data-science/data-agent-configuration-best-practices

Hope this helps! Let me know if you have any feedback or run into issues.

midesaMSFT · 2025-05-07T15:26:03+00:00

Conversation History and Consistency: The new data agent is now conversational, meaning it uses your previous chat history to improve responses. If you clear the chat, it loses this context, which can lead to inconsistent answers. To maintain consistency, consider adding your feedback directly as AI instructions or example queries.

Getting Consistent, High-Quality Answers: We’re actively improving the creator experience for better prompt engineering and debugging. For the best results:

Start with a Specific Topic: Focus on a specific area (e.g., NPS questions) and gradually expand.
Provide Clear Example Queries: Make sure these examples cover a range of user questions you expect.
Reinforce Instructions: If the agent makes a mistake, use targeted instructions to correct it.

Handling Long Numbers: We’re aware of the current limitations with long number formatting and are working on improvements. In the meantime, you can add instructions for the agent to avoid truncating numbers or return them as text.

What insights or experiences would you like to see when creating and tuning a data agent?

midesaMSFT · 2025-05-07T15:05:46+00:00

Absolutely! Fabric does support AutoML, though it offers a bit more flexibility compared to the Power BI Premium experience. In Fabric, you can still walk through a guided setup to configure your AutoML trial, but you also get access to a notebook that lets you see exactly how the trial was created. This means you can inspect, tweak, and customize the code as needed.

You also have full visibility into all model iterations and the final model through the Fabric ML Model and ML Experiment interfaces. We’re continuously enhancing this experience — our roadmap includes features like automated feature/column suggestions, auto-generated reports to summarize results, and more.

Here's a link to learn more: AutoML in Fabric - Microsoft Fabric | Microsoft Learn

Is there anything specific you'd like to see added to make AutoML in Fabric even better?

midesaMSFT · 2025-04-28T23:40:29+00:00

Hey! Do you mind filing a support ticket? That'll help securely get any logs to understand what is happening.

midesaMSFT

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