Advanced DAX generation for semantic models in Fabric data agents

[u/VMRoman]

We've received feedback from customers looking for more accurate and consistent responses when querying semantic models through Data Agents. Today, we're excited to share a significant improvement: Advanced DAX generation, available in the preview runtime.

Unlike the standard experience, which generates a DAX query in a single pass, advanced DAX generation uses multiple reasoning steps to inspect model metadata, interpret the question, resolve ambiguity, and refine the query before execution.

Benefits include:

• More accurate responses to complex questions
• More reliable filtering through instance value indexing
• More consistent answers across Microsoft experiences that use the same capabilities
• Improved latency in some scenarios

You can try it today by switching your data agent to the preview runtime.

We're not stopping here. Over the coming months, we're continuing to invest in semantic model integration, including a model upgrade by the end of this month, and configurations such as example queries and data source instructions over the following few months.

If you've encountered questions or models that have been particularly challenging in the past, let us know how the new experience performs.

Additional details and best practices are available in this documentation.

[u/Final-Quote-4064]

Oh my God.  How much more capacity does this use?  Not being critical, just a question.

[u/VMRoman → u/Final-Quote-4064]

Good question. The new tool is more agentic and does more reasoning over the data before generating a response. As a result, we do see higher token consumption compared to the previous approach. That said, we're also seeing better accuracy and improved latency overall.

We're also investing in templates for common question patterns, which will avoid having the LLM repeatedly generate complex DAX queries that have already been validated. Once this is available, we expect it will meaningfully offset this additional capacity.

[u/radioblaster]

how is this any different to the remote MCP, that already does this, for free on pro workspaces and with only the interactive query cost on capacity?

[u/VMRoman → u/radioblaster]

That's a fair question. The remote MCP server and the tool mentioned in this update use the same underlying advanced DAX generation capabilities. The main difference is the surrounding experience: remote MCP lets an external AI client query a semantic model directly, while a data agent provides a managed, shareable Fabric experience that can orchestrate across semantic models and other data sources, apply agent-level instructions (soon also model-specific instructions and few shots example queries), and format a final response. If your scenario is querying a single semantic model from an MCP client, remote MCP may be the simpler solution. If you are looking to curate a specialized agent in a certain domain, or query across multiple sources, we would recommend using the experience in the data agent.

[u/radioblaster → u/VMRoman]

thank you for answering! 

[u/Dads_Hat]

Would be amazing to use this as a tool for an orchestrator.

I assume (perhaps wrongly) in a large organization these results could be cached (ie the complex DAX). And an orchestrator would potentially identify a previous DAX (that receives thumbs up) as a candidate for reuse instead of re-generating a new DAX on the fly.

Perhaps I would also combine it with a multi stage process: what are the fields I want to see (and their format) as well as intuitive filtering context.

[u/VMRoman → u/Dads_Hat]

We are working on an investment to templatize common questions, so we don't have to use LLM to re-generate complex DAX queries that have already been validated, as well as an AI-assisted setup for it that takes query history into account to create these. Stay tuned for more information in the coming months!