Beyond the Ontology: How the Rest of Fabric IQ Turns Meaning into Action

Yesterday we went deep on Fabric IQ’s Ontology—the shared vocabulary that teaches Microsoft Fabric how your business actually talks. Today we’ll zoom out to everything else: the graph that lets insights travel across relationships, the agents that answer questions and watch your operations in real time, and the governance and integration that make it usable at scale.

From unified data to unified intelligence (quick re‑anchor)

Fabric IQ is a workload (preview) in Microsoft Fabric that organizes data already in OneLake—lakehouses, eventhouses, and existing semantic models—according to the language of your business. That shared meaning is then exposed to analytics, copilots, and applications so definitions travel with the data. As of mid‑November 2025, IQ remains in preview.

Graph in Fabric: relationship‑native analysis at enterprise scale

Where the ontology defines what things are and how they relate, Graph in Microsoft Fabric makes those relationships first‑class for analysis. It provides a labeled property graph over your OneLake tables, with native support for the emerging GQL standard and built‑in graph algorithms—so you can traverse multi‑hop questions (impact chains, communities, “what else is connected?”) without staging data into a separate graph store. It’s tightly integrated with Fabric security, governance, and Power BI. Status: preview (docs updated November 18, 2025).

The practical punchline: once your ontology binds to real data, graph queries let you ask operational questions that dimensional models alone struggle with—think Order → Route → Asset → Sensor for cold‑chain risk, or Customer → Household → Product for influence paths. Graph lives where your data already is—OneLake—so you avoid duplicative pipelines.

Data Agents: conversational Q&A grounded in your estate

Fabric Data Agents let people ask plain‑English questions over Fabric sources—lakehouses, warehouses, Power BI semantic models, KQL databases, and ontologies—and get grounded, governed answers. You create one in your Fabric workspace and attach the sources you trust. For broader reach, you can consume a data agent inside Microsoft Copilot Studio as a connected agent, enabling agent‑to‑agent collaboration while preserving permissions and provenance. Status:preview.

The upshot: this is where shared meaning becomes accessible. Users stop guessing table names and start asking business questions; agents stop hallucinating and start citing. When paired with your ontology, you get answers that honor the same definitions across BI and AI.

Operations Agents: from awareness to recommended action

If data agents answer questions, operations agents keep watch. Built in Fabric’s Real‑Time Intelligence, an operations agent continuously monitors streaming signals, applies rules and context, and proposes actions—escalations, reroutes, resets—under governance. This is the “sense‑and‑respond” loop many teams have tried to script with brittle rules; now it can sit on the same semantics you use elsewhere. Status: preview.

How it all fits (and why it matters)

• OneLake remains your single logical lake.
• Ontology expresses the business meaning over that data.
• Graph operationalizes relationships for multi‑hop reasoning.
• Data agents make that meaning discoverable in natural language and can participate in Microsoft 365 surfaces via Copilot Studio.
• Operations agents watch live signals and recommend actions aligned to that same meaning.

Microsoft is also framing Fabric IQ alongside Work IQ (the intelligence layer for Microsoft 365) and Foundry IQ (a knowledge layer for agent grounding). Together they’re pitched as a shared intelligence layer for data, productivity, and agents. In other words: consistent semantics for both humans and autonomous workflows.

Governance and cost, briefly

Because IQ, Graph, and agents live inside Fabric, they inherit Fabric’s capacity‑based model and governance posture. Plan for standard Fabric capacity plus OneLake storage economics; align workspaces to capacities and use existing admin patterns for permissions, lineage, and monitoring. For many teams, this means you extend—not replace—how you already govern Power BI and Fabric workloads.

A pragmatic pilot for “the rest of IQ”

1) Make a single decision loop your north star. Example: prevent cold‑chain spoilage during lane disruptions.

2) Stand up a minimal graph. Use the ontology‑bound tables to define nodes/edges for Shipment, Route, Asset, Sensor. Validate two or three traversals you care about (“show affected orders if lane X has a breach”).

3) Add a data agent. Connect the lakehouse and the ontology so analysts can query exposure and mitigations in plain language.

4) Add an operations agent. Point it at the eventhouse or KQL source to watch real‑time exceptions and propose a reroute playbook.

5) Measure the lift. Track time‑to‑answer, time‑to‑mitigation, and the rate of duplicate definitions. Expand only after one loop shows sustained gains.

Bottom line

Yesterday’s ontology work gave your data a shared vocabulary. Today’s additions—Graph for traversals, Data Agentsfor grounded answers, and Operations Agents for real‑time recommendations—turn that vocabulary into decisions. Start small: wire a graph to one decision, put a data agent in front of it, let an operations agent watch the stream, and measure the deltas. That’s how meaning becomes momentum.