Operations Context for AI | Ontology in Fabric IQ
Generate a full business ontology from an existing Power BI semantic model, map entities and relationships, and embed real-time operational signals alongside business rules that live inside the ontology with the data and its meaning. From there, trace cascading operational impacts across your business through the relationship graph, stand up Operations Agents in natural language with Teams-based actions, and connect the same ontology as a knowledge source in Copilot Studio or Azure AI Foundry. Chafia Aouissi, Fabric IQ Principal PM Manager, shares how to model your business operations, embed intelligence in your data, and deploy agents that act on it. Logic lives with your data. Embed business rules directly in the Fabric IQ ontology. Define thresholds, trigger notifications, and cascade decisions through connected entities. Watch the demo. Surface cascading impacts with a single query. Filter the Fabric IQ relationship graph by any entity and trace downstream operational impact across your entire business. Check out a built-in ontology graph in Fabric IQ. Build a Fabric IQ Operations agent in natural language. Define monitoring goals, connect your ontology as its knowledge base, configure Teams actions, & deploy. See the full build. QUICK LINKS: 00:00 — Unify models & data with Fabric IQ 01:12 — Generate an ontology 02:27 — Bring in Power BI reports 03:08 — View across multiple data sources 04:23 — Define rules 05:18 — Built-in ontology graph 06:03 — Fabric IQ agents 08:24 — Fabric IQ as Knowledge Source 08:54 — Wrap up Link References Get started at https://aka.ms/FabricIQ Unfamiliar with Microsoft Mechanics? As Microsoft’s official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast Keep getting this insider knowledge, join us on social: Follow us on Twitter: https://twitter.com/MSFTMechanics Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics Video Transcript: -The agents you build and use need the right operational context of how your business runs to deliver the best outcomes. Today, that context is often fragmented across systems, defined differently by different teams, or buried in dashboards and logic, making outcomes inconsistent and agent behavior hard to predict. That’s where Microsoft Fabric IQ comes in. Fabric IQ introduces a semantic foundation that unifies models and data through an ontology. It defines the shared business entities and their relationships, and connects them to your data. It provides the operational context needed to understand how the business actually runs, without altering any underlying data. Analysts can not only work with the data they already trust, but also model how the business works. And agents can use that same shared context to reason and act more consistently. Today, I’ll show you both sides. -First, how a data analyst leverages Fabric IQ inside a Fabric workspace, using ontology to model the business concepts. Then, how Fabric IQ uses the same context to drive more reliable and predictable insights from agents. I’ll start from the point of view of an analyst looking to create a full fidelity view of how an airline operates, including processes such as ticketing, maintenance, and more. The first thing I need to do is to create a new ontology. I can either build one from scratch, or jumpstart by using an existing Power BI semantic model. As you see here, there’s a new option to generate an ontology from this semantic model. I just need to choose the workspace, give it a name. I’ll choose AirlineOperationsOntology. Then confirm by hitting Create. In just a few clicks, I’m able to see the different entities of our airline business. All data is now linked not only through keys, but also business relationships and semantics. We can see flights, airlines, routes, and more. If I click into airports, because Fabric IQ is semantically aware of the relationships between entities, it shows the routes connected to runways which are in turn connected to airports. -And for any entity, I can choose to add more live operational signals. In this case, I want to add details about the runway conditions using real-time data, including contamination, visual range for visibility, as well as the available cleared width and more. And you can also bring in your Power BI reports for a canonical view of how to monitor and manage these aircrafts. From the ontology, I’ll head over to the Report links tab that opens the OneLake catalog with all of my reports. I’ll search for air and there are three matching reports for gates, ground service, plus safety and runway. So I’ll add them and hit Connect to confirm. -So, in just a few clicks, we’ve expanded our ontology with the live operational view, using real-time signal, geospatial data, and more. Now, as an analyst, I can work immediately with it, and our agents can act on it as well. Let’s fast‑forward and see what I’ve unlocked. You can see that I now have a richer view over my data, which is connected to real‑world operations. We’re no longer optimizing one report or one dataset at a time. We’re looking at our operations across multiple data sources, in the language of our business, with meaning and relationships already understood. -Now let me show you how this makes it easier to turn insights into concrete decisions and actions. First, in the flight entity type overview, I can see how it relates to my other business processes. I see entities like bookings, gates, airlines, and more as a graph. I have links to all of my connected Power BI reports. There is a real-time weather data, including wind knots, as well as geo-spatial insights showing all of my flights. Using Fabric Maps, I have a fleet level view of all my active flights, and I can see live air traffic across the fleet. This, in fact, is a heat map view of three New York City area airports, and we can see that JFK in this case is impacted with lots of runway activity. -I can now understand the system as a whole, across bookings, flights, airports, and real‑time conditions. And I can drill in further to understand what is going on: I have opened the runways entity, and you’ll remember some of these categories from before. Since there’s snow in the area, I can immediately see the runway conditions that affect operations, things like surface friction and contamination levels, so I understand how safe it is for planes to take off and land. -Beyond connecting raw data, I can also define rules directly in the ontology, so this logic lives with the data and its business meaning, instead of being hard coded somewhere else. In this case, I’ll add a rule that says if runway contamination exceeds high threshold value of 25%, notify the passengers proactively of upcoming delays. We’ll also notify the ground crew, so they know that the runways need to be cleared. The rules are now embedded in the ontology, and the value comes from seeing how runway conditions impact the rest of the operations. That’s where the built‑in ontology graph helps. Let’s look at the relationship graph. I’ll expand the graph view. And add a filter for JFK airport Then run the query. No code needed here. And I get a filtered view for JFK. And immediately, I can see a poor condition that’s affecting Runway 25R. -From that insight, it’s easy to see the downstream impact. This runway issue is already affecting related gates and baggage operations that will need to be rescheduled. This is a unified view of our entire operations and how connected events will cascade across related business entities. This is how ontology helps you as an analyst. But remember, the same operational context is also available to AI agents, no matter how you build them. -Let me demonstrate this in the context of one our built-in Fabric IQ agents. From the New Item catalog, you can find the built-in agents by searching for agent. There is a Data agent designed to answer questions, and an Operations agent designed for real-time data and business action recommendations. The Operations agent is a perfect fit for our airline operations scenario, so I’ll choose that one. I want this agent to help with runway-related analysis and actions, so I’ll name it RunwayConditionsAgent, leave the location, and create it. -From there, I can add a bit more information to set up the agent, like adding the business goals for what it should accomplish I want this one to monitor surface conditions for runways and ensure things run smoothly based on logic like we used before. In fact, in the Agent instructions, using natural language, no code, I’ll describe that if surface contamination is reported above 10%, send ground crews to take care of it. Likewise, the clear width should be more than 25 meters, and the agent should send ground crew to visually assess whether planes can safely brake. -Now let’s add some knowledge. And for that, I’ll choose our AirlineOntology. And here’s where I can add actions. I’ll add one to assign ground crew for clearing, along with description for what needs to be done. Then I’ll give it the Runway ID as the one to clear and the Temperature to predict the type of clearing needed. And Create to add that one. Now I’ll add another for requesting visual assessment, and perform similar steps for the parameters. These will send status updates in Microsoft Teams. Now everything is defined and ready. I just need to save this new agent. That takes a moment. And once it’s finished, it creates a nice agent playbook with what it’s designed to do. -Now, with the agent running, the right people will get notified of what to do in Microsoft Teams Here, I’m looking at the Operations agent It’s alerting us that Runway 29L has only 22 meters of clear path. This is under our 25 meter threshold. It recommends to deploy the ground crew for a runway clearance operation. As the human in the loop, I can choose whether or not to proceed with the recommendation. I’ll do that. -Then it asks to confirm a few details. They look good, so I’ll confirm, and the ground crew is on its way. And here is the good news. If you’re building your own agent in Microsoft Copilot Studio or using Microsoft Foundry, Fabric IQ ontology will be an integrated knowledge source that you will be able to choose from. As you choose your knowledge types, you can select Fabric IQ. This will ground agents in the same semantic foundation that already runs your operations. And of course, the agents you build and connect to Fabric IQ will respect the permissions and security policies you already use in Fabric today. -As I have shown, Microsoft Fabric IQ gives agents shared understanding, with entities, relationships, rules, and actions so they can move from insight to decision more reliably. To learn more and get started, check out aka.ms/FabricIQ. Keep watching Microsoft Mechanics for the latest news and deep dives. And thank you for watching.
