Microsoft is turning Fabric, its integrated data platform, into the place enterprise agents go to learn how a company works, whether that’s what happened last quarter or what’s happening on the factory floor right now.

On Tuesday, at its FabCon and SQLCon data conference in Barcelona, Spain, Microsoft outlined the next step in its vision for how the products that make up Fabric can simplify all of this information gathering.

Microsoft, for example, described how Fabric IQ, the context layer inside Fabric, now feeds Microsoft 365 Copilot by default. Outside agents can query it over MCP. Power BI will soon be able to turn the same definitions into apps. And ontologies, which add business rules to the mix, moved further into preview.

As Microsoft Fabric CTO Amir Netz said in a press briefing after the keynote, “Agents are a very, very strange animal, and I always like to say it’s like Drew Barrymore from 50 First Dates. Every time they open their eyes, they forget everything that happened before.

“They don’t know where they are, so the first thing we have to do is tell them where they are. You are now working for Microsoft. You are now working for Wells Fargo. You are now working for Emirates.”

“Agents are a very, very strange animal, and I always like to say it’s like Drew Barrymore from 50 First Dates. Every time they open their eyes, they forget everything that happened before.”

Customers tend to find this out the hard way. Yitzhak Kesselman, the corporate vice president who runs Fabric IQ, tells The New Stack that companies unify their data, run models on it, and then look at the answers.

“Customers that are more advanced on their journey have their own evals for their agent,” he says, adding that what they see sometimes isn’t what they expected. “Then [customers] will understand: ‘Okay, now I need to create the context for my agents.'”

Kesselman says he met with more than 320 companies last year, and the pressure to get there comes from the business side, which asks to “‘show me the value of those agents,'” he says, “‘the before and after.'”

Arun Ulag, Microsoft’s executive vice president for Azure Data, said in the keynote that coding agents work because they have “the code, the repos, the change history, the specs, the tests.” But outside of coding, most enterprises have nothing comparable, he argued.

Fabric IQ is one of four pieces of what Microsoft calls Microsoft IQ (this is Microsoft, after all, so there’s always a lot of different names and products involved).

Work IQ covers email, Teams, and SharePoint. Foundry IQ covers documents and manuals. Web IQ covers the public internet.

Fabric IQ, Ulag said, “focuses on the state of your business and how your business actually runs.” His announcement blog post describes it as combining “unified data from OneLake, trusted metrics from Power BI semantic models, and operational context from ontologies and real-time intelligence.”

Getting the data in

“Everyone wants to jump to AI, but then they realize, ‘Oh, I need data for it,'” Kesselman says. “They need data from their business applications, their structured data. They need to have real-time data.”

OneLake, Fabric’s storage layer, is at the core of all of this, and for the system to provide context, enterprises have to feed it data from across all the first-party and third-party services they use.

Fabric’s shortcuts and mirroring, which connect or replicate external sources into OneLake, are free of charge, and Netz said storage under management in OneLake is growing 300% year over year.

What’s new this week at FabCon and SQLCon Barcelona 2026

On Tuesday, the company announced a two-way integration with Salesforce Data Cloud 360, mirroring from Google BigQuery in general availability, a ClickHouse workload that runs that engine directly against OneLake, and dbt’s Fusion engine, due in Fabric in the next couple of weeks.

Simply copying data won’t satisfy security teams, though. Mirrored security roles, which reach public preview in the coming weeks, bring permissions along with the data. Access roles defined in Snowflake, for example, will show up in Fabric with the same members and the same table permissions.

As Microsoft’s Shireen Bahadur, who demonstrated the feature in the keynote, put it, “We’re not just solely replicating security rules. We’re actually actively enforcing and preserving those roles.”

“We’re not just solely replicating security rules. We’re actually actively enforcing and preserving those roles.”

OneLake isn’t a one-way street. Enterprises can also pull data out and use it in other tools.

OneLake data, after all, is stored in open formats and has open APIs, and Netz said on stage that any engine that reads them can use it. “If you want to use this data that we help people to get for free with our competitors’ tools, go for it. I’m not happy about it, but go for it,” he said.

IQ sharing, now in preview, lets an organization share governed tables, files, Markdown agent instructions, and RDF ontologies with another tenant without copying, with an expiration date. That’s something Fabric users have wanted for a while, and the keynote announcement drew plenty of applause from the thousands of data professionals in attendance.

Past, present, and future

“Not only what we have in the past, not only what’s happening in the present. We also want the agents to understand what we want to happen in the future,” Netz said in the keynote.

The past is semantic models. A semantic model is the layer under every Power BI report that defines how a metric like revenue is calculated, how entities relate, and which tables supply the numbers. Netz said Power BI users have already created 22 million, and Ulag called them “the core of Fabric IQ.”

The present is real-time intelligence, Fabric’s streaming and event stack, which Kesselman also runs. He recalls two CIOs telling him the same thing that day in Paris. “‘There is no AI without RTI;’ there’s no AI without real-time intelligence,” he says. “If you don’t have the streaming data… data that is fresh, you will run your LLMs on data which is like hours or days old.”

“‘There is no AI without RTI;’ there’s no AI without real-time intelligence.”

A signal on its own isn’t enough, though: “You want to take the signal that you get now… the event that comes now, and look historically, okay, is it an anomaly or not?” Kesselman says. That’s why the keynote showed batch copy jobs and event streams feeding each other, so an agent can judge what just happened against what usually happens.

The future is Fabric Planning, which went generally available in July and got a performance and feature update this week. It’s Microsoft’s tool for budgets, forecasts, and targets, the numbers a business wants to hit rather than the ones it has already recorded.

A plan is a Fabric item like a lakehouse or a notebook, and it borrows its measures from the same semantic models the reports use, so revenue means the same thing in the forecast as it does in the dashboard. In the demo, a change to the inputs cascaded through a model of about 13 million cells.

What makes these work is ontologies: Formal descriptions of the things a business deals with, how they relate, and the rules for handling them. Netz called them “semantic models plus plus” on stage, and in a later press briefing, he used an airline to explain the plus.

“If you are an airline, you have planes, you have pilots, you have ground crews, you have airports, you have luggage,” he said. Those are the entities.

The relationships between them “are way more than data relationships,” he said. “It’s not only to say, ‘Oh, I have a match with a foreign key and a primary key between a pilot and a plane,’ but it could be a policy relationship. Which pilot can fly which plane based on their certification, or is the pilot allowed to fly this plane right now based on the rest hours in the last 24 hours?”

But ontologies don’t only define nouns; they also define verbs. “I can ground a plane. I can redirect the plane. I can assign a plane a gate.”

Creating those ontologies, which have to map the various terms that a company might use for the same thing to a single entity, can be extremely time-consuming. But since they are so core to this project, and to allowing agents to reason over data, Microsoft built a tool to generate them automatically.

Putting it to work

Fabric IQ in Microsoft 365 Copilot Chat and Cowork, the mode for delegating multistep tasks that Microsoft built on the technology behind Anthropic’s Claude Cowork, is generally available as of Tuesday. It answers business questions from Power BI semantic models and reports; it’s on by default for Fabric and Power BI customers, and Microsoft says it doesn’t consume additional AI tokens.

But business users don’t just want to chat with these models. In this age of vibecoding, they also want to generate applications that use all of this data.

Power BI Desktop is getting an app-creation experience in preview in the coming weeks. Users start from a semantic model, describe the app, and then have Copilot generate and publish it to Fabric.

Unlike with a report, Ulag wrote, these apps “can accept inputs, write back data, preserve shared state, and support operational workflows.”

They’re Fabric Apps built on Rayfin, the open-source SDK Microsoft introduced at Build. Power BI Pro and Premium Per User customers get them with a Fabric database of up to 1GB per app at no additional cost.

Netz confirmed in the briefing that “there’s no core difference” between the two, and Ulag added, “What gets created is a Fabric app, right? And that’s it.”

For agents outside Copilot, Fabric IQ MCP is generally available now, with six read-only tools for finding semantic models and reports, reading their schemas, and running DAX queries against them.

Ontology MCP tools, currently in preview, expose ontology definitions and queries. Fabric data agents can now, also in preview, use an ontology as their context source. And the context reaches Microsoft Foundry, Copilot Studio, and GitHub Copilot through the same layer.

Developers can drive the new data engineering agent from their own tools.

Built on the Osmos technology Microsoft acquired in January, it takes on long-running work like migrations and ETL, and can be started and steered from GitHub Copilot, VS Code, Codex, and Claude Code.

Frontier models will write that code, Netz said on stage, and “it won’t fail,” but you don’t know whether the results are right.

Everyone has a context layer now

This year, Databricks shipped Genie Ontology, Snowflake shipped a Horizon Context Layer, Google shipped a Knowledge Catalog, and Salesforce shipped a headless Data 360, which it describes as a context service. Databricks CEO Ali Ghodsi summed up the pitch at his own conference in June as AI having a context problem rather than an intelligence problem.

Microsoft’s version turns on by default inside Microsoft 365 Copilot and starts from 22 million semantic models. Microsoft also said this week it will contribute to Apache Ossie, the vendor-neutral semantic metadata standard Snowflake started, and that it wants DAX, Power BI’s formula language, recognized as an Ossie query language.

When Microsoft made the context argument at Build in June, the pieces that let an agent act on that context were mostly on a roadmap. Now the answers are generally available, and the actions are in preview.

Asked how Fabric copes when the user is no longer one person running a dashboard but a fleet of agents, Kesselman points to observability.

“Fabric really allows you to have this kind of holy grail of combination of both kinds of system data, how the agent runs, but also the business data,” he says, so a company can check whether an agent did what it was supposed to do and what that did to the business. “There’s more in this area that will come in the next few months.”

The post Microsoft Fabric is where AI agents learn how the business works appeared first on The New Stack.

If you rely too heavily on one AI provider, switching can mean rebuilding workloads and moving data while you face untangling integrations. A new vendor-neutral group aims to help organizations keep their options open. That’s the goal of the Eclipse Foundation, which announced the formation of the Sovereign AI Foundation on Wednesday.

The new body will serve as a vendor-neutral initiative to help member organizations manage AI platform lock-in, monitor cyber risk, and maintain control of their data. The group has been active on Eclipse’s project site since July.

An initial community of 17 organizations comes together to advance AI sovereignty through open-source collaboration.

The foundation’s mission is to help organizations “navigate AI dependencies” (as in dependence on any single AI service, not control over where application dependencies get tied to a particular model’s DNA… although, by extension, that too), where dependence can limit a software engineering team’s ability to control where data is processed, choose which technologies to use, or change direction as costs, requirements, and provider terms evolve.

A core rationale for AI sovereignty control

Executive director at the Eclipse Foundation, Mike Milinkovich, tells The New Stack that the risk of not adopting an open approach to AI sovereignty control depends on how deeply AI is embedded in an organization and how difficult the underlying systems are to change.

“The deeper the dependency, the greater the potential risk,” Milinkovich says. “But however embedded the underlying systems are, the costs show up in predictable places: higher prices that make switching difficult, and the challenge of rebuilding integrations, moving data and workloads, documenting systems after the fact, compliance delays, and operational disruption.”

He underlines his point and says part of the Sovereign AI Foundation’s work will be to “compare real-world experiences” and build stronger evidence around those risks.

“The deeper the dependency, the greater the potential risk. But however embedded the underlying systems are, the costs show up in predictable places.”

The Sovereign AI Foundation will work to give members an open forum to understand AI dependencies, compare experiences, and develop practical guidance for decisions about data, models, infrastructure, and operations.

Can we move beyond the AI hype-cycle now?

Acknowledging what he calls the pre-IPO hype cycle around the promotion of frontier models, Milinkovich calls for a concerted focus on where the real value in AI lies: enterprise and industrial applications that deliver measurable business value, without vendor lock-in.

“AI will not deliver on its potential if it is based entirely on a dependence on a relatively small number of providers for critical AI models, platforms, and infrastructure,” Milinkovich insists. “As AI becomes part of the systems organizations rely on every day, that dependence can leave them with too little visibility into where their data goes, which models use it, or how easily they can switch when costs, terms, or requirements change.”

Choice, control &  freedom of action

He asks us to understand how far the Sovereign AI Foundation will go to find practical ways to “preserve choice and control,” with a vision of delivering open-source and open-weight solutions that allow freedom of action.

As admirable and philanthropic as this sounds — and coming from a Brussels-based foundation whose founding members are mostly European — is there any America-first idealism here, given that open-weight Chinese models are also openly available?

“This is not about China or any single provider. The same questions apply to AI technologies from the United States, Europe, Asia, or anywhere else,” confirms Milinkovich. “Closed models can limit visibility, while access to model weights alone does not provide the transparency, control, or the independent governance that organizations may need. The real question for our members is whether they can understand, adapt, and, if necessary, replace the systems they depend on.”

17 founding members

As noted above, this initiative launches with 17 participating organizations spanning technology, industry, research, and the open source community: France’s CEA LIST,  Thales and Kentyou, Germany’s EclipseSource, Open Elements, TypeFox, Vector Informatik, and Bosch, Italy’s Engineering Ingegneria Informatica and Eurotech, Sweden’s Ericsson, India’s Infosys, Belgium’s KU Leuven, Japan’s Renesas Electronics, Malaysia’s The IO Foundation… and then there’s North Carolina’s Red Hat, and the University of York in the UK.

The Sovereign AI Foundation focuses on five areas:

• Mapping real-world use cases: Building a shared view of how members deploy, evaluate, and pilot open-source AI.
• Assessing technologies and trends: Monitoring developments across models, evaluation frameworks, inference infrastructure, and regulation.
• Developing practical resources: Producing whitepapers, blueprints, landscape analyses, and reference materials.
• Building a peer community: Connecting organizations at every stage of open source AI adoption.
• Translating research into action: Turning outcomes from Eclipse Foundation research initiatives, including EU-funded projects, into insights that members can apply.

A mission to tackle data control, cyber safety, or lock-in?

There’s a lot to unpack here in an organization only just out of the blocks; it begs the question whether the Sovereign AI Foundation will focus on data control challenges and efficiency, cyber safety, or platform lock-in challenges.

“It’s all three, and they reinforce one another,” Milinkovich says. “It is an efficiency issue if moving to a better or less expensive model requires rebuilding the workload. It is a security issue if organizations lack the visibility needed to assess risk. And it is a lock-in and resilience issue if critical operations become so tied to one provider that changing direction is costly or disruptive.”

“The need here is clearest wherever AI is used in systems with long lifecycles, demanding safety and security requirements, or sensitive data.”

He explains that “the specific needs vary”, so a manufacturer may need AI to keep operating when connectivity is limited, while a public agency may need to explain how an AI-supported decision was made.

“The need here is clearest wherever AI is used in systems with long lifecycles, demanding safety and security requirements, or sensitive data. That includes automotive, manufacturing, telecommunications, aerospace and defense, energy, and the public sector,” adds Milinkovich.

Interest will extend well beyond Europe

He confirms that the foundation’s initial participants do “reflect that mix”, as companies including Bosch, Vector, Renesas, Ericsson, and Thales put sovereignty high on the agenda. But he qualifies that dependence on critical AI technologies is a global concern, “so we expect interest well beyond Europe” in the weeks and months ahead.

Milinkovich offers a selection of still-forming use cases that he thinks are already solid. 

• EclipseSource is working with industrial customers on AI-enabled development tools like Eclipse Theia AI, where teams need flexibility over models and deployment.
• The Open VSX Registry provides vendor-neutral access to thousands of extensions for a growing range of open source and AI-enabled development tools.
• Eurotech is using Eclipse-governed projects to bring AI to the edge, where systems interact with physical equipment and must remain secure, reliable, and maintainable for years.
• Eclipse LMOS, originally developed at Deutsche Telekom, supports production multi-agent systems at significant scale and underpins multiple global deployments today.

“Across these examples, the common approach is modularity: keeping models, infrastructure, and applications separate enough that one can change without rebuilding everything. Our job is to capture what works and turn it into guidance others can use,” affirmed Milinkovich.

The Sovereign AI Foundation team says that alongside the 17 founding members, additional organizations are in the process of joining, and it invites eligible Eclipse Foundation member organizations to participate.

The post Eclipse wants companies to be free to switch AI providers. Today, doing so can mean a costly rebuild. appeared first on The New Stack.