Semantic intelligence is a vital aspect of really understanding what knowledge means and the way it may be used.
Microsoft is now deeply integrating semantics and ontologies into its Cloth knowledge 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 deal with a elementary drawback with enterprise AI brokers: Effectiveness relies upon not simply on dataset dimension however on how properly knowledge 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 knowledge platform technique, which lately built-in LinkedIn's graph database expertise to supply context.
Microsoft can be increasing its knowledge 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 at the moment are usually obtainable.
"After I take into consideration what material does for purchasers, it provides clients a unified knowledge 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 Knowledge at Microsoft.
Why semantic understanding issues for AI brokers
Conventional AI brokers wrestle with a elementary limitation: they will see patterns in knowledge however don't perceive what that knowledge represents in enterprise phrases. An agent may analyze gross sales transactions with out understanding buyer hierarchies, seasonal patterns or product relationships. It could possibly 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 knowledge and enterprise that means is what causes unreliable predictions and poor automated choices. 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 technology (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 data. They perceive relationships like which suppliers present which merchandise, how manufacturing strains hook up with stock techniques 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; they usually hook up with various knowledge sources throughout Azure, AWS, Google Cloud, on-premises techniques, and SaaS platforms like Dynamics 365.
"We’ve 20 million semantic fashions that run in material as we speak. 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 knowledge that they care about? What are the metrics that they care about? How does the info relate to one another?"
The limitation of those semantic fashions has been their scope. They labored properly for enterprise intelligence, analytics, and visualization, however they solely operated inside particular person reviews or departmental boundaries. Cloth IQ removes these constraints.
"Nevertheless, we've had a spot. These semantic fashions have been solely used for BI use instances," Ulag stated. "There's a a lot greater 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 adjustments what organizations can do with enterprise context and that means. "What does it do if you happen to improve them into an ontology? What occurs is that now you’ll be able to join knowledge throughout your enterprise," Ulag stated.
He defined that the ontology additionally integrates with real-time knowledge streams. Past connecting knowledge, ontologies enable organizations to outline operational guidelines. This mixture creates the muse for operational brokers that perceive enterprise context at a stage that conventional AI techniques can not obtain. Cross-enterprise knowledge connections work along with real-time integration and rule definitions.
Operational brokers that perceive and act on enterprise operations
Cloth IQ allows a brand new class of brokers Microsoft calls "operational brokers." These brokers can autonomously monitor knowledge and take motion based mostly on the ontology's understanding of enterprise operations.
"We're additionally introducing one thing referred to as operations brokers in material that may watch your knowledge for you, that may watch the foundations that you just're asking it to watch. And it may autonomously take motion beneath human supervision," Ulag stated.
Ulag offered a provide chain instance that illustrates the distinction from conventional approaches. A company can mannequin its provide chain and supply operations within the ontology. When real-time knowledge exhibits congestion in a part of a metropolis, the operational agent can routinely reroute vans round the issue.
The ontologies created in Cloth IQ combine instantly with Microsoft's agent growth platforms. This offers enterprise context that makes brokers extra dependable and correct.
"It actually takes the work that we've carried out in semantic fashions in material with unified knowledge to a totally completely different stage, permitting clients 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 higher allow agentic AI.
Semantics and their related ontologies just do that and extra. Context is about understanding why a request is being made, and semantics perceive the deeper that 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 vital 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 is just not sufficient. Upgrading current semantic fashions into operational ontologies might present a quicker path to dependable brokers.
