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In 2026, data engineers working with multi-agent systems will face a familiar problem: agents created on different platforms do not operate from a shared understanding of the business. The result is not a failure of the model: it is a hallucination driven by a fragmented context.
The problem is that agents built on different platforms, by different teams, don’t share a common understanding of how the business actually operates. Each one carries their own interpretation of what a customer, an order or a region means. When those definitions diverge across a workforce of agents, decisions fail.
A series of announcements from Microsoft this week point directly to that problem. The centerpiece is a significant expansion of Fabric IQthe semantic intelligence layer that the company debuted in November 2025. The Fabric IQ enterprise ontology is now accessible through MCP to any agent from any vendor, not just Microsoft. On top of that, Microsoft is adding business planning to Fabric IQ, unifying historical data, real-time signals, and formal organizational goals into one queryable layer. The new Database Hub brings together Azure SQL, Cosmos DB, PostgreSQL, MySQL, and SQL Server under a single management plane within Fabric. Fabric data brokers reach general availability.
The overall goal is a unified platform where all data and semantics are available and accessible to any agent to obtain the context that companies require.
Amir Netz, CTO of Microsoft Fabric, used a film analogy to explain why the shared context layer is important. "She’s a bit like the girl from 50 First Dates," Netz told VentureBeat. "Every morning they wake up and forget everything and have to explain it again. This is the explanation you give them every morning."
Why access to MCP changes the equation
Making the MCP ontology accessible is the step that moves Fabric IQ from a Fabric-specific feature to a shared infrastructure for multi-vendor agent deployments. Netz was explicit about the design intent.
"It doesn’t really matter whose agent it is, how it was created, what the role is," Netz said. "There is some common knowledge, some common context that all agents will share."
That shared context is also where Netz draws a clear line between what ontology does and what RAG does. He did not rule out augmented recovery generation as a technique: he specifically located it. RAG handles large volumes of documents, such as regulations, company manuals and technical documentation, where on-demand retrieval is more practical than loading everything in context.
"We don’t expect humans to remember everything by heart," said. "When someone asks a question, you have to know how to go and do a little search, find the relevant and appropriate part and bring it back."
But RAG doesn’t solve the state of business in real time, he argued. It doesn’t tell an agent which planes are in the air right now, whether the crew has enough rest hours, or what the current priority is on a given product line.
"The mistake of the past was that they thought that one technology could give you everything." Netz said. "The cognitive model of agents is similar to that of humans. You have to have things that are available out of memory, things that are available on demand, things that are constantly observed and detected in real time."
Analysts say Microsoft has yet to close execution gap
Industry analysts see the logic behind Microsoft’s direction, but have questions about what comes next.
Robert Kramer, an analyst at Moor Insights and Strategy, said Microsoft’s broad stack gives it a structural advantage in the race to become the default platform for enterprise agent deployments.
"Fabric links to Power BI, Microsoft 365, Dynamics, and Azure services. That gives Microsoft a natural path to connect enterprise data to business users, operational workflows, and now the AI systems that operate in that environment." said. The trade-off, Kramer said, is that Microsoft is competing on a broader surface than Databricks or Snowflake, which built their reputation on the depth of the data platform itself.
The most immediate question for data teams, Kramer said, is whether access to MCP actually reduces integration work.
"Most companies do not operate in a single AI environment. Finance could be using one set of tools, designing another, supply chain something else," Kramer told VentureBeat. "If Fabric IQ can act as a common data context layer that those agents can access, it begins to reduce some of the fragmentation that typically appears around enterprise data."
But, he said, "If you simply add another protocol that still requires a lot of engineering work, adoption will be slower."
It is open to debate whether the engineering work is the more difficult problem. Independent analyst Sanjeev Mohan told VentureBeat that the biggest challenge is organizational, not technical.
"I don’t think you fully understand the implications yet," said about enterprise data teams. "This is a classic excess of capabilities: capabilities are expanding faster than people imagine using them. The most difficult job will be to ensure that the context layer is reliable and trustworthy."
Holger Mueller, principal analyst at Constellation Research, believes that the MCP is the right mechanism, but recommends caution in its implementation.
"For companies to benefit from AI, they need to have access to their data (which in many places is disorganized and isolated) and they want to do it in a way that makes it easy for AI to get there in a standard way. That’s what MCP does," Mueller told VentureBeat. "The devil is in the details. How good the access is, how well it works and how much it costs. Access and governance still need to be resolved."
The Database Center and the competitive landscape
The Fabric IQ announcements arrive alongside Database Hub, now in early access, which brings together Azure SQL, Azure Cosmos DB, PostgreSQL, MySQL, and SQL Server under a single management and observability layer within Fabric. The intent is to give data operations teams a place to monitor, govern, and optimize their database without changing the way each service is deployed.
Devin Pratt, research director at IDC, said the integrated direction follows where the overall market is headed. IDC expects that by 2029, 60% of enterprise data platforms will unify transactional and analytical workloads.
"Microsoft’s approach is to bring more of those pieces together in a coordinated approach, while rivals are moving along similar lines from different starting points." Pratt told VentureBeat.
What this means for enterprise data teams
For data engineers responsible for preparing pipelines for AI, the practical implication of this week’s announcements is a change in where hard work lives. Connecting data sources to a platform is a solved problem. Defining what that data means in business terms and making that definition constantly available to every agent who queries it is not.
That change has a concrete implication for data professionals. The semantic layer (the ontology that maps business entities, relationships, and operational rules) is becoming production infrastructure. It will need to be built, versioned, governed and maintained with the same discipline as a data pipeline. This is a new category of responsibility for data engineering teams, and most organizations don’t yet have the staff or structure for it.
The broader trend reflected by this week’s announcements is that the data platform race in 2026 is no longer primarily about compute or storage. It’s about which platform can offer the most reliable shared context to the widest range of agents.
