Semantic intelligence is a important aspect of truly understanding what information means and the way it may be used.
Microsoft is now deeply integrating semantics and ontologies into its Cloth information platform with its new Cloth IQ expertise that it debuted on the Microsoft Ignite convention Tuesday.
Cloth IQ is a semantic intelligence layer designed to handle a elementary downside with enterprise AI brokers: Effectiveness relies upon not simply on dataset measurement however on how nicely information displays precise enterprise operations. The brand new expertise creates a shared semantic construction that maps datasets to real-world entities, their relationships, hierarchies, and operational context. The semantic layer represents the most recent step in Microsoft's information platform technique, which just lately built-in LinkedIn's graph database expertise to supply context.
Microsoft can be increasing its information portfolio with a sequence of recent companies: Azure HorizonDB, a PostgreSQL-compatible service in early preview, in addition to SQL Server 2025 and Azure DocumentDB, which are actually usually accessible.
"Once I take into consideration what cloth does for patrons, it offers prospects a unified information platform in order that they don't need to sew collectively many, many, many alternative instruments to get to enterprise worth," stated Arun Ulag, company vp of Azure Information at Microsoft.
Why semantic understanding issues for AI brokers
Conventional AI brokers battle with a elementary limitation: they’ll see patterns in information however don't perceive what that information represents in enterprise phrases. An agent may analyze gross sales transactions with out understanding buyer hierarchies, seasonal patterns or product relationships. It might probably question stock ranges with out figuring out how manufacturing strains hook up with distribution networks or how provider relationships have an effect on availability.
This hole between uncooked information and enterprise which means is what causes unreliable predictions and poor automated selections. Ulag defined that Cloth IQ addresses this by offering a semantic layer that captures how organizations truly function.
This architectural strategy differs considerably from retrieval-augmented era (RAG) and vector database methods that opponents have emphasised.
Whereas RAG pulls related paperwork to supply context, Cloth IQ creates a persistent semantic graph representing organizational construction, workflows and enterprise logic. Brokers don't simply retrieve info. They perceive relationships like which suppliers present which merchandise, how manufacturing strains hook up with stock programs or how buyer hierarchies map to gross sales territories.
From analytics semantic fashions to operational ontologies
Microsoft has invested in semantic fashions for over a decade by way of Energy BI. These fashions encapsulate enterprise logic and outline entities and relationships; they specify metrics and hierarchies; and so they hook up with numerous information sources throughout Azure, AWS, Google Cloud, on-premises programs, and SaaS platforms like Dynamics 365.
"We’ve 20 million semantic fashions that run in cloth in the present day. Why? As a result of we constructed the semantic modeling layer into Energy BI. So behind each Energy BI report is a semantic mannequin," Ulag stated. "These semantic fashions already encapsulate quite a lot of the enterprise logic that mirrors what a buyer cares about. What’s the information that they care about? What are the metrics that they care about? How does the information relate to one another?"
The limitation of those semantic fashions has been their scope. They labored nicely for enterprise intelligence, analytics, and visualization, however they solely operated inside particular person experiences or departmental boundaries. Cloth IQ removes these constraints.
"Nonetheless, we've had a spot. These semantic fashions had been solely used for BI use instances," Ulag stated. "There's a a lot larger alternative on the market, which is the chance to have the ability to take these semantic fashions and improve them right into a full ontology."
Upgrading the semantic fashions to ontologies essentially modifications what organizations can do with enterprise context and which means. "What does it do when you improve them into an ontology? What occurs is that now you possibly can join information throughout your enterprise," Ulag stated.
He defined that the ontology additionally integrates with real-time information streams. Past connecting information, ontologies enable organizations to outline operational guidelines. This mix creates the muse for operational brokers that perceive enterprise context at a degree that conventional AI programs can’t obtain. Cross-enterprise information connections work along with real-time integration and rule definitions.
Operational brokers that perceive and act on enterprise operations
Cloth IQ permits a brand new class of brokers Microsoft calls "operational brokers." These brokers can autonomously monitor information and take motion based mostly on the ontology's understanding of enterprise operations.
"We're additionally introducing one thing known as operations brokers in cloth that may watch your information for you, that may watch the foundations that you simply're asking it to observe. And it may possibly autonomously take motion beneath human supervision," Ulag stated.
Ulag supplied a provide chain instance that illustrates the distinction from conventional approaches. A corporation can mannequin its provide chain and supply operations within the ontology. When real-time information reveals congestion in a part of a metropolis, the operational agent can mechanically reroute vehicles round the issue.
The ontologies created in Cloth IQ combine instantly with Microsoft's agent growth platforms. This supplies enterprise context that makes brokers extra dependable and correct.
"It actually takes the work that we've performed in semantic fashions in cloth with unified information to a totally completely different degree, permitting prospects to have the ability to mannequin their operations and take enterprise actions," Ulag stated.
What this implies for enterprise AI methods
There appears to be a want for context engineering to raised allow agentic AI.
Semantics and their related ontologies do exactly that and extra. Context is about understanding why a request is being made, and semantics perceive the deeper which means. For enterprises battling AI agent reliability regardless of giant datasets, Cloth IQ represents a essentially completely different strategy. It strikes past scaling compute or fine-tuning fashions. The important query is whether or not enterprise context captured in ontologies would enhance agent effectiveness greater than conventional optimization paths.
The strategic guess Microsoft is making is evident: Semantic understanding of enterprise operations determines AI agent effectiveness. Entry to giant datasets alone isn’t sufficient. Upgrading current semantic fashions into operational ontologies may present a sooner path to dependable brokers.
