Multiple Semantic Models for one Ontology Layer
Hello everyone, When generating an ontology layer, I see that you can generate one from a semantic model. However, I see I can import multiple warehouses and lakehouses as part of an ontology layer if I wanted to start from scratch. Let's say I have four semantic models that each represent an important busines process (finance, sales, inventory, production/procurement). Each semantic model follows a well-defined star schema, and are mature enough to even be considered data products. If I want to eventually create an agent where I access data from both sales and inventory semantic model, would the best practice be to import/use both semantic models in the ontology layer? Or should I create one ontology layer per semantic model, and have my agent consume the information by attaching both ontology layers to said agent? For this example, let's say I want an agent that can see sales history, customer purchasing behavior history from the sales model, and can see inventory of products being ordered fro the inventory model. Back to the original question, what's the best practice ultimately for an agent to consume information from multiple semantic models?
Fabric Data Agent: “Query cancelled” on aggregations
Good evening, I’m trying to build a Data Agent for my organization, even though the feature is still in preview. Its source is an ontology. I’m currently working with only six tables, but they are very large. I’m having an issue when asking the Data Agent questions that require grouping and aggregation, such as COUNT or SUM. In those cases, I often get the following error: "Query cancelled by upstream caller Status Code: Cancelled" The agent suggests that this may be caused by the amount of resources required by the query, which seems plausible. I also tried using smaller tables in the ontology. This improved things somewhat, and some queries now work, but aggregations are still extremely slow and I still frequently receive the same error. Is this expected behavior with large ontologies and aggregation queries, or could there be some configuration or setting that needs to be changed? Is there anything I can do to improve the performance of GROUP BY, COUNT, and SUM queries over an ontology-backed Data Agent? EDIT: I also found the following error in one of the question's execution log: 'The request was canceled due to the configured HttpClient.Timeout of 300 seconds elapsing.'AI Agents Collaboration: How Copilot Studio Agent can work with Fabric Agent
A new breakthrough in the world of AI Agents For the first time, we’re bringing Copilot Studio Agent and Fabric Data Agent together in one session — showing how collaboration between AI Agents can reshape data experiences. Join us for: AI Agents Collaboration: How Copilot Studio Agent can work with Fabric Agent Session Highlights: Introduction to Fabric Data Agent Understanding the difference between Fabric Agent and Copilot Step-by-step guide to building a Fabric Data Agent Introduction to Copilot Studio Agent How Copilot Studio Agent collaborates with Fabric Data Agent Live Demo: Email-Driven Insights – Integrating Copilot Studio with Fabric Agent Discover how Copilot Studio can automate email-based data requests and deliver quick insights through Fabric Data Agent. Sponsored by Data Crafters Collaboration with Data Scientist Development Finally Join us in Sudan Microsoft Fabric User Group and Follow UsFrom Data to Intelligence: How Fabric IQ Powers AI Agents
AI agents can help automation and insight - but without business context, they often fail. Enter Microsoft Fabric IQ, a new semantic intelligence layer that transforms Fabric from a data platform into an intelligence platform. Fabric IQ organizes enterprise data around business concepts, not tables, creating a shared ontology of entities, relationships, and rules linked to real-time data in OneLake. This enables AI agents to reason, interpret, and act with accuracy, eliminating the semantic fragmentation that causes unreliable decisions. In this session, we’ll: - Explain what Fabric IQ is and why it matters for AI adoption. - Show how its semantic layer powers agentic AI with examples. - Demo how an AI agent uses Fabric IQ to answer complex business questions. If you’re exploring AI agents or building intelligent apps, this session will show you how Fabric IQ provides the semantic foundation for trustworthy, context-aware automation.Global Fabric Day 2026
A day filled with Fabric stuff! Agenda Time Session 09:00 – 09:15 Welcome 09:15 – 09:55 Patrick, SDK/Ateko – Fabric RTI, Real-Time Reporting 09:55 – 10:05 Break 10:05 – 10:45 Christian Cote, SMS Equipment – Azure DevOps integration with Fabric 10:45 – 10:55 Break 10:55 – 11:35 Kateryna Cherniak, SilverCreek Software – DQ fuzzy matching in Fabric 11:35 – 11:45 Break 11:45 – 12:25 Thiago Valentin, Go Auto - TBD 12:25 – 12:45 Lunch, Close and Socials (Raffles? Prizes?) 12:45 – 13:00 Buffer / Wrap-up
Fabric Data Agent chat error: “The natural language query could not be processed” (Ontology source)
Hi all, I’m having a persistent issue with Fabric Data Agent when using an Ontology as the data source. In the chat, even very simple natural language questions fail with: “The request is invalid. The natural language query could not be processed. Please rephrase your query and try again.” RAID examples: db24d651-d4f8-4dd9-8fcb-bab105ee68eb, bfb8cd2a-676a-40a0-9c91-f36ca250ff07. What I already validated: Ontology and graph are created correctly. Relationships exist and are active. Manual graph queries execute successfully. The issue happens in chat NL queries (NL-to-GQL translation), even with minimal prompts. I recreated the Data Agent from scratch, same behavior. So this looks like a chat/NL parser issue rather than a data or graph execution issue. Has anyone found a reliable workaround, or is this a known service-side issue in preview? Any guidance from Microsoft team would be very helpful. Thanks!data agent can't get data from ontology data source
Hello! I created an entity type in an ontology and successfully added a binding. The data is visible on the entity’s Instances page. However, when I use “Build agent with AI” to ask very simple questions about this ontology, it consistently returns a response similar to the one shown in the attached screenshot, referring to a “table” or "schema" As far as I understand, an ontology does not have the concept of tables. Is my understanding incorrect? Do I need to select or configure an ontology table somewhere before the agent can query the data? In addition, when I use "Test data agent" to query the ontology data, the request hangs for several minutes and then returns an error.
Retirement of Fabric data agent integration in Copilot in Power BI
https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Retirement-of-Fabric-data-agent-integration-in-Copilot-in-Power/ba-p/5328344 I'm not sure I understand this announcement. I think it means that we will no longer be able to use Copilot from the screenshot below to access data agents/ IQ ontologies. Can someone please confirm? Any other thoughts around the implication of this announcement?
