Fabric IQ brings business meaning to AI agents - so they can act, not just predict
Most AI agents see patterns. Operations needs agents that see the business. Microsoft's new Fabric IQ adds a semantic intelligence layer to Microsoft Fabric that maps your data to real entities, relationships, hierarchies, and operational context.
The goal is simple: agents that reflect how your organization actually runs. That means fewer brittle automations, fewer wrong calls, and more decisions you'd trust a seasoned ops lead to make.
Why pattern-matching agents fail in operations
Agents can analyze sales transactions yet miss customer hierarchies, seasonal demand, or product substitutions. They can read inventory levels yet ignore how production lines, DC capacity, and supplier lead times connect.
That gap between raw data and business meaning is why forecasts drift, alerts misfire, and automations break in the real world.
What Fabric IQ changes
Fabric IQ creates a shared semantic structure - a persistent graph of your organization's entities (customers, SKUs, suppliers, assets), the relationships between them, and the rules that govern how work flows. It's different from retrieval-augmented generation (RAG) and vector search.
RAG pulls documents for context. Fabric IQ encodes the business itself. Agents don't just retrieve facts; they operate with context like "which suppliers feed which plants," "how lines roll up to work centers," or "how customer hierarchies map to territories."
Built on years of semantic modeling
Microsoft has shipped semantic models for years through Power BI. Those models already capture metrics, hierarchies, and logic across cloud, on-prem, and SaaS data sources.
"We have 20 million semantic models that run in Fabric today," said Arun Ulag, corporate vice president of Azure Data at Microsoft. "These semantic models already encapsulate a lot of the business logic that mirrors what a customer cares about."
Until now, those models lived mostly in BI. Fabric IQ upgrades them into operational ontologies that connect across departments, integrate with real-time streams, and add business rules.
Operational agents that monitor and act
With Fabric IQ, Microsoft is introducing "operational agents" that watch data, monitor rules you define, and take action under human supervision.
Ulag shared a supply chain example: model your delivery network in the ontology; when live traffic shows a blockage in part of a city, the agent automatically reroutes trucks to protect service levels. The ontology plugs into Microsoft's agent development platforms, so the actions reflect your actual operations, not just a keyword match.
"It really takes the work that we've done in semantic models in Fabric with unified data to a completely different level, allowing customers to be able to model their operations and take business actions," Ulag said.
Why this matters for ops leaders
- Fewer brittle automations: Decisions are grounded in process logic and relationships, not just columns and IDs.
- Faster time to value: Upgrade existing Power BI models into ontologies instead of starting from scratch.
- Better guardrails: Encode rules (SLAs, compliance, escalation paths) once and apply them across agents.
- Closer to real-time: Tie in IoT, ERP, WMS, TMS, and streaming data so actions align with what's happening now.
RAG vs. ontologies: use both, for different jobs
Use RAG when the task depends on unstructured knowledge (SOPs, contracts, manuals). Use ontologies when the task depends on how the business runs (who supplies what, how operations roll up, what rules apply).
In practice, you'll combine them: RAG for policy reference, ontology for decision logic, and agents to execute.
How to pilot Fabric IQ in your operation
- Map the problem: Pick one high-impact flow with repeatable decisions (inventory exceptions, carrier selection, line changeovers).
- Start from what you have: Inventory existing Power BI semantic models; list core entities, relationships, and KPIs.
- Add rules and streams: Define constraints and actions; connect real-time feeds from ERP/WMS/TMS/IoT.
- Stand up one operational agent: Give it a narrow scope, human-in-the-loop approvals, and clear rollback paths.
- Measure and iterate: Track fill rate, OTIF, inventory turns, or downtime against a baseline before expanding.
Ecosystem updates worth noting
- LinkedIn graph tech is now integrated into Microsoft's data platform strategy to add more context to enterprise data.
- Azure HorizonDB (PostgreSQL-compatible) enters early preview, while SQL Server 2025 and Azure DocumentDB are generally available - giving ops teams more options for the systems behind their ontologies and agents.
The strategic bet
Microsoft's position is blunt: access to large datasets isn't enough. AI agents become reliable when they reflect how your business actually works. Upgrading semantic models into operational ontologies - and wiring them to real-time data and rules - is a faster path to agents you'll trust in production.
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