In 2026, data engineers working with multi-agent systems face a familiar problem: agents built on different platforms do not work from a common understanding of the business. The result is not model failure—it’s hallucinations driven by 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 works. Each carries its own interpretation of what the customer, order or region means. When these definitions differ among a workforce of agents, decisions are broken.
A number of announcements from Microsoft this week take direct aim at this problem. The center is a significant expansion Fabric IQsemantic intelligence layer that the company is debuting in November 2025. Fabric IQ’s business ontology is now available through MCP to any agent from any vendor, not just Microsoft. In addition, Microsoft is adding enterprise planning to Fabric IQ, combining historical data, real-time alerts, and formal organizational goals in one layer that can be queried. The new Database Hub brings Azure SQL, Cosmos DB, PostgreSQL, MySQL and SQL Server under a single management plane within Fabric. Fabric data agents reach general availability.
The overall goal is a single platform where all the data and semantics are available and accessible by any agent to get the context that enterprises require.
Amir Netz, CTO of Microsoft Fabric, used a movie analogy to explain why a shared context layer is important. "It’s a bit like the girl on 50 First Dates." Netz told VentureBeat. "Every morning they wake up and forget everything and you have to explain it again. This is the explanation you give them every morning."
Why does MCP input change Eq
Making Ontology available to MCP is a step that moves Fabric IQ from a Fabric-specific feature to a shared infrastructure for multi-vendor agent deployments. Netz has been open about his design intent.
"It doesn’t matter who the agent is, how it’s set up, what its role is." Netz said. "All agents have some common knowledge, some common context to share."
This shared context is also where Netz draws a clear line between ontology and what RAG does. He didn’t dismiss search-augmented generation as a technique—he specifically positioned it. RAG manages large document bodies such as regulations, company manuals and technical documents, where searching on demand is more practical than loading everything into context.
"We don’t expect people to memorize everything," he said. "When someone asks a question, you have to know how to go and do a little searching, find the right one, and bring it back."
However, he argued that RAG does not address the real-time business situation. It doesn’t tell the agent what planes are currently in the air, whether the crew has enough rest hours, or what the current priority is in a particular product line.
"The mistake of the past was thinking that one technology could give you everything." Netz said. "The cognitive model of agents is similar to humans. You have to have things that are out of memory, things that are on demand, things that are constantly being observed and discovered in real time."
Executive gap analysts say Microsoft still needs to be shut down
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, noted that Microsoft’s broad stack gives it a structural advantage in the race to become the standard platform for enterprise agent deployments.
"Fabric connects to Power BI, Microsoft 365, Dynamics, and Azure services. This gives Microsoft a natural way to connect enterprise data with business users, operational workflows, and the AI systems now running in that environment." he said. The tradeoff, Cramer said, is that Microsoft is competing on a much larger surface area than Databricks or Snowflake, which built their reputations on the depth of the data platform itself.
A more pressing question for data teams, Kramer said, is whether MCP access actually reduces integration work.
"Most enterprises do not operate in a single AI environment. Finance may use one set of tools, engineering another, something else in the supply chain," Kramer told VentureBeat. "If Fabric IQ can act as a common data context layer that those agents can tap into, it begins to reduce some of the fragmentation typically seen around enterprise data."
But he said "If it adds another protocol that still requires a lot of engineering work, adoption will be slower."
Whether engineering is a more difficult challenge is open to debate. Sanjeev Mohan, an independent analyst, told VentureBeat that the bigger challenge is organizational rather than technical.
"I don’t think they fully understand the implications yet." he said of enterprise data groups. "This is classic opportunity overshooting—opportunities are expanding faster than people can imagine using them. The more difficult task will be to ensure that the context layer is valid and reliable."
Holger Mueller, senior analyst at Constellation Research, sees the MCP as the right mechanism, but urges caution in implementation.
"For businesses to benefit from AI, they need access to their data — which is disorganized, messy in many places — and they want AI to make it easy to get there by default. MCP does this," Mueller told VentureBeat. "The devil is in the details. How good is access, how well does it work and what does it cost. Access and management still need to be sorted out."
Database Hub and competitive image
The Fabric IQ announcements come alongside Database Hub, now in early access, which brings Azure SQL, Azure Cosmos DB, PostgreSQL, MySQL and SQL Server under a single management and observability layer within Fabric. The goal is to give data operations teams a place to monitor, manage and optimize their database assets without changing how each service is deployed.
IDC research director Devin Pratt said the integrated direction follows where the broader market is headed. IDC expects that by 2029 60% of corporate data platforms will combine transactional and analytical workloads.
"Microsoft’s angle is to bring more of these pieces together in a coordinated approach, while competitors move along similar lines from different starting points." Pratt told VentureBeat.
What this means for enterprise data groups
For data engineers responsible for making pipelines AI-ready, the practical implication of this week’s announcements is a shift in where the hard work lives. Connecting data sources to the platform is a solved problem. It’s not about defining what that information means from a business perspective and making that definition consistently available to every agent who queries it.
This change has specific implications for data professionals. The semantic layer – an ontology that maps business entities, relationships and operational rules – becomes the production infrastructure. It should be built, versioned, managed and maintained with the same discipline as the data pipeline. This is a new category of responsibility for data engineering teams, and most organizations are not yet staffed or structured for it.
The broader trend of 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 deliver the most reliable shared context to the widest range of agents.
