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.'
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?Let Fabric Data Agent ontology inherit RLS from Semantic Model roles instead of OneLake Security
I'm building a Fabric Data Agent with an ontology on top of a Lakehouse (Customer 360 style model — dimension and fact tables). I also have a Power BI semantic model over the same data with a DAX RLS role already defined (e.g., restricting a "Germany" role to rows for that region). I expected the ontology/Data Agent to honor that semantic model role when a user with the role queries it. Instead, I found that the ontology queries the Lakehouse directly via a graph engine (GQL), and semantic model DAX RLS only applies within the scope of that semantic model — it has no effect on the ontology path. The only way to get row-level filtering enforced there is OneLake Security (data access roles), which requires defining access — including RLS predicates — separately, per table/folder, in the "Manage OneLake security" experience. The problem: This means the same business rule ("this role sees only Germany rows") has to be defined and maintained twice, in two different places, using two different mechanisms: Once as a DAX RLS role in the semantic model (for Power BI reports) Once as OneLake Security data access roles, configured per table, for the ontology/Data Agent path For a model with many related tables (dimensions + facts), this means recreating the same predicate across every table individually in OneLake Security, rather than defining the rule once at the role level and having it apply consistently across all related tables — the way a single semantic model role does. What I'd like to see: I'd prefer that semantic model roles (and their RLS/CLS definitions) can be inherited by, or reused in, the ontology — so one role definition governs both the semantic model and the Data Agent/ontology layer, instead of maintaining a parallel, per-table configuration in OneLake Security. Today it feels like there's no single source of truth for "who sees what" when a Lakehouse is consumed through multiple paths (Power BI vs. Data Agent). Questions for the community/product team: Is there a supported way today to have the ontology consume a semantic model as its data source (rather than Lakehouse tables directly), so DAX RLS is respected end-to-end? Is unifying RLS definition across semantic model and OneLake Security on the roadmap, or is the expectation that these stay as two independently maintained layers? If OneLake Security is meant to be the single enforcement layer going forward, is there a way to define a role once and have it apply across a set of related tables (dimensions + facts) rather than configuring RLS per table? Any guidance — or confirmation this is a known gap — would be appreciated. My Goal is to achieve RLS for Data Agents without having to define rules for each Table which what the Role DAX in Semantic Model offer .
Data Agent gives correct GQL Query but is unable to give output
Hi All, I have a Data Agent connected to a Graph data source that keeps giving error: I'm unable to directly retrieve the data due to a technical issue translating the request to a graph query. However, here is the correct GQL query you can use to get .... The GQL query provided by the Data Agent works perfectly on the Graph instance but the Data Agent is not giving the output. Not sure what is the exact error and where can I see to troubleshoot it. Both Data Agent and Graph are in same workspace (even same folder for that matter).How does Fabric Data Agent works
I have created a data agent, when I asked the agent the following questions: what are the most profitable products in 2024? I expect the agent to rank 2024 sales revenue by product name, but instead the agent rank sales revenue by product name, product category and brands. I can't figure out where this error comes from because I checked agent instrcutions and example queries, we have never trained the agent to rank by those additional column. If I check into the details of the agent response, it shows my questions was transated into the following text: Analyzed Lakehouse Lakehouse for: "List the top 10 most profitable products in 2024 by total GrossMarginEUR. Show ProductName, Category, Brand, total GrossMarginEUR, total RevenueEUR, and average GrossMarginPct. Only include active products. Order by total GrossMarginEUR descending." So according to the text, the agent has been instructed to rank by product categry and brand. But where is this text from? It seems I can not correct it in the data agent?
