This summary is produced by the author, and not by AI.
This article references the Tabular Editor 2 CLI for background. The current command-line interface is the Tabular Editor 3 CLI, described below.
If you work with Power BI, Fabric, or Analysis Services, then you likely are aware of (or already use) AI with semantic models in some capacity. Be it helping you write DAX or M code, querying your model, or even end-to-end agentic and asynchronous development. If you want an introduction to agentic development with semantic models, you can read about it in our previous series.
Agentic development can save you time and help you produce better semantic models. Therefore, we anticipate that AI and agents will play a major role in semantic model development (as well as BI and data analytics as a whole). It’s clear that AI and agents will augment BI professionals and self-service users to build better models, visualizations, and end-to-end data solutions in a fraction of the time that it used to take. In our opinion, this value is demonstrated already for early adopters of other tools: the Fabric command-line interface (CLI), semantic model MCP servers released by Maxim Anatsko and Microsoft, or just AI skills that promote agentic development.
Our mission is to provide professionals the best tools available for building, optimizing, and managing semantic models. We will make sure that both you and the agents that you use and orchestrate can use these tools to get the best possible semantic models quickly and efficiently.
In this article, we share our view of tooling for agentic development of semantic models. We also announce the release of a built-in AI assistant integrated in the Tabular Editor 3 UI, and a new, enhanced Tabular Editor CLI for sophisticated end-to-end development.
If you do not want to or can’t use AI and agents in your own development work, then that is fine. However, we would encourage you to remain open-minded and explore these new tools; we are already past the time of “slop and vibes”, provided that you invest in the right tools and context.
For agents to perform well on any task, they need the right tools and context. This is also true with semantic models, where the agent needs ways to interact with a semantic model and good context about the relevant concepts, techniques, and workflows.
Like traditional semantic model development, there is no “best” tool; each tool is useful for different scenarios and addressing different problems. We see this as follows:
In the previous GIF, you can see Claude Code in the terminal, discovering, exploring, querying, and modifying a model open in Power BI Desktop without an MCP server or any other tools. It’s not a prescriptive workflow; Claude just figures it out.
This is an emerging area, so it’s unclear how this space will look in a few years… or even a few months. However, we want to ensure that any approaches we take to AI integration will benefit both human users and agents both equally and effectively.
In the March release of Tabular Editor, we’re releasing the AI assistant. This is a new feature that lets you connect an LLM and let it perform certain tasks in Tabular Editor:
Here’s an interactive walkthrough of the AI assistant, teaching you how to set up and use it.
The following is a brief demonstration:
The AI assistant is intended to be an entry-level helper for areas where we’ve seen people use AI with Tabular Editor. The purpose of the AI assistant is to make these more advanced or time-consuming tasks more convenient for those who choose to use AI. The assistant isn’t designed to be as broad as an MCP server or coding agent; rather, to make certain parts of the tool more accessible.
The most interesting application of the AI assistant is to help you write C# scripts. These scripts let the assistant make changes to the semantic model in Tabular Editor, which means that you can see the changes in real-time, and Tabular Editor validates them. You also can undo any changes with Ctrl+Z and preview those changes in a new model “diff” view. The best part, however, is that working scripts can be generalized to re-use as saved scripts or macros without AI at all… so you can quite easily build up a library of re-usable macros without knowing or writing any C# code.
The diff view is particularly interesting, as it’s a new view in Tabular Editor that will eventually allow you to compare two semantic models. In the future, we intend this to work just like other popular diff tools, such as ALM toolkit. For the March release, however, the model diff view only works with C# scripts, irrespective of how you author them (i.e. with or without AI).
However, we also want to ensure that you can ignore the assistant altogether, if you choose to. You can choose to install the AI assistant or not when you update Tabular Editor. Furthermore, this feature will never bother or intrude on your work, and when you close it, it stays closed. You’re fully in control.
With the AI assistant, we’ve opted for a bring your own model approach. This means that you must supply connection information in the preferences, like an API key. This affords you the flexibility to choose providers, models, and even use local or organizational LLMs if available. This is particularly helpful to organizations that have stricter compliance or residency requirements.
You cannot use your subscriptions for other coding agents and AI tools (like Claude, OpenAI, GitHub Copilot) with the AI assistant, as that’s against their terms of service.
These personal subscriptions are also cheaper than using the APIs, because they're subsidized. Therefore, using the same model in a coding agent with your subscription is cheaper than using it in the AI assistant via an API key. There are users who may prefer to use GitHub Copilot or Claude Code; for these users, we recommend you read about the Tabular Editor semantic model CLI in the next section, as this tool is specifically made to be the best possible semantic model tool for these agents.
To change the provider or model, you just must click the “gear” icon. You can choose from multiple providers, including Anthropic, OpenAI, and OpenRouter. For local and organizational LLMs, see our documentation for a full walkthrough of how to set this up.
The AI assistant also supports custom instructions, which function like skills. These are simple markdown files which you can add to an AI folder:
These instructions are conditionally loaded to the assistant based on keywords that you configure, or you can invoke them like you would in other tools by pressing “/”. The AI assistant comes with several custom instructions preloaded. You can see some examples, below:
The purpose of these custom instructions is to provide good context to improve the outputs that you get from the AI assistant. Some of these instructions are workflows, like organize-model or optimize-model. Others are general information to teach the LLM about important concepts related to semantic models, like model-design.
Notably, the AI assistant isn’t designed to let you do everything in Tabular Editor. Here’s some examples of what you can’t do:
To reiterate, the AI assistant is meant to be a helper with advanced tasks that we’ve observed cost users time or require specific skills, like writing DAX queries or C# scripts. The AI assistant isn’t designed or intended for more complex or end-to-end agentic development scenarios. For these scenarios, we’ll provide a new, improved semantic model command-line interface (CLI).
In April, we’ll introduce a new semantic model command-line interface (CLI) in preview that’s built for human developers but optimized for agents:
A CLI can be used by humans manually or headlessly in CI/CD pipelines. An example of this is the older Tabular Editor 2 CLI, which is used by many people to automate the development, testing, and deployment of semantic models. However, CLI tools can also be used by agents. We’ve written about several use-cases of this with the Fabric CLI, which is (in the author’s opinion!) the best Power BI or Fabric feature Microsoft released in 2025.
We’d like to announce a new semantic model CLI that has all the features that you expect and need to develop, manage, and optimize semantic models… and some extras. It’s been designed from the ground up to provide you everything that’s in Tabular Editor, and even new, more advanced features specifically designed for agentic development scenarios.
The biggest feature of this CLI is that it works on Windows, Mac, and Linux. This means that you can now develop, manage, and optimize your semantic models from any operating system:
In the screenshot, you can see the te command line connecting to a semantic model, and then running the VertiPaq Analyzer to identify memory bottlenecks. The CLI is running on macOS in the cmux terminal, manually.
The CLI has full backward compatibility with the older Tabular Editor CLI. However, it’s also been enhanced with a variety of new commands and features.
While the CLI is technically fully backward compatible, it’s using the enhanced scripting and BPA engine of Tabular Editor 3. Therefore, you will still want to test it rigorously before integrating it into your pipelines. Note that schema compare will not be available at the time of first release.
First, to introduce it, you can concisely browse and summarize a model:
As well as query a model with DAX, and even get query traces or benchmarks like you would with DAX Studio:
This means that the CLI can be a valuable tool not just for writing DAX but also optimizing it in the hands of both humans and agents. It provides a rich set of tools for exploring and querying your model in an elegant and effective way but also one that will still be token-lean and straightforward for AI coding agents, too.
Of course, you can also develop a semantic model with this CLI. Search, find, and modify any object or property, individually or in bulk. You also have all of the typical features of Tabular Editor, like formatting DAX and even Power Query:
The tool has full access to the functionality in Tabular Editor that you know and love, like the VertiPaq Analyzer for memory optimization, the Best Practice Analyzer for finding and resolving issues and Code Actions, which provide smart warnings and can even enable auto-fix on model save and deploy:
What’s also unique to the CLI is that you can configure skills to help agents better use the CLI and develop semantic models. This agent setup works with any provider, from Claude Code to GitHub Copilot, and can be scoped to the user or project level. We’ve tested the CLI with various coding agents and experienced positive results, and we look forward to hearing your experiences, as well:
In this demonstration, you can see Claude Code using the Tabular Editor CLI without any skills or context to identify optimization opportunities. It can do this quite well out-of-the-box because it has access to the tools in the Tabular Editor UI designed and used for optimization, and familiar to the LLM from training data, alone. With skills this becomes even more powerful.
To reiterate, the CLI is previewed in this article, but isn't yet available. It will be available in our next release in April.
These are just some of the features of the AI assistant and this new semantic model CLI; less than one fifth of what’s possible. The best is yet to come…
We anticipate that agents will become a key part of semantic model development in the future. Already today, we see many examples of agents enhancing or expediting semantic model development workflows. We’re releasing two new features:
We hope these new features help you develop better semantic models, faster!
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