Fabric Data Agent
A Fabric Data Agent is a configured domain expert that answers questions in plain English over your lakehouse, warehouse, Power BI semantic model or KQL database. We build the agent — and the trustworthy data foundation underneath it that decides whether its answers are right.
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What we hear from operators
The problems we solve
The agent answers confidently, and wrongly
A data agent built on an ambiguous semantic model gives fluent, confident answers that are subtly wrong — two definitions of margin, a measure that filters differently than the user assumes. In operations, a wrong answer delivered with confidence is worse than no answer. The fix is not a better prompt; it is a governed semantic model the agent can only interpret one way.
The data the agent needs is scattered
A useful agent needs to reason across production, sales, inventory and finance. If those live in SAP, a separate MES, spreadsheets and a warehouse that do not meet, the agent can only answer the narrow questions its one source supports. The value is cross-domain, and cross-domain needs the data unified in OneLake first.
No governance means the agent leaks
An agent that ignores row-level security will happily tell a regional manager the numbers for a region they should not see. Without RLS, sensitivity labels and a governed grounding set, a data agent is a data-exfiltration risk wearing a friendly interface. Governance is a build requirement, not an afterthought.
The pilot never reaches production
A data agent demoed on a laptop is easy; one running in production for the whole operation needs a paid F2 or higher Fabric capacity, tenant settings enabled, service-principal authentication for app access, and a publishing model so other people and apps can call it. Teams that skip this stall at the demo and never ship.
Who this is for
Who fabric data agent is built for
The roles that feel the problem first — and what we build for each of them.
Operations Director
The problem
Answers to shift-level questions arrive hours late through the analyst queue, after the decision window has closed.
What we build
A Fabric Data Agent grounded on the production semantic model, answering OEE, downtime and output questions in Teams in plain English.
Shift questions answered in seconds, not hours
Supply Chain Head
The problem
Inventory, OTIF and supplier questions need a cross-system pull every time, so nobody asks the ad-hoc question that would catch a problem early.
What we build
A data agent spanning inventory, orders and supplier data unified in OneLake, callable during the planning meeting.
Cross-domain answers without a data pull
Head of BI / Data
The problem
The team is buried in one-off data requests and the well-built semantic model is used by only a handful of people.
What we build
A published, governed Fabric Data Agent over the existing semantic model, with RLS enforced and accuracy validated against test questions.
Self-serve Q&A on the governed model
CIO
The problem
Business units want AI over company data but IT is wary of a chatbot that leaks or hallucinates.
What we build
A Fabric Data Agent with Purview sensitivity, RLS, service-principal access and cross-geo settings appropriate to the region.
Governed AI access without exfiltration risk
Measurable outcomes
What changes after implementation
Specific shifts from delivered fabric data agent work — the before, and the after.
Question to answer: analyst backlog → plain-English answer in seconds
Operations and planning staff ask a question and get a grounded answer from the data, instead of joining the analyst's queue and waiting for a one-off pull.
Analyst load: one-off data requests reduced as self-serve Q&A takes hold
The routine "can you pull me this number" requests move to the agent, and the analyst team's time shifts to the work only they can do.
Answers governed: agent respects RLS and sensitivity, not just intent
Every answer honours the same row-level security and data boundaries as your governed reports, so wider access does not mean wider leakage.
From demo to production: F2+ capacity, service principals, published endpoint
The agent runs as a callable production service other apps and agents can use, not a laptop demo that never ships.
By market
Fabric Data Agent — market-specific pages
Each page below covers what fabric data agent looks like specifically in that market — the local ERP landscape, compliance context, and the operational patterns we actually see there.
Singapore & Malaysia
United Kingdom
North America
Technology stack
Common questions
Fabric Data Agent — frequently asked
What is a Microsoft Fabric Data Agent?
It is a configured, domain-specific virtual analyst that answers plain-English questions over selected Fabric data — a lakehouse, warehouse, Power BI semantic model, KQL database, mirrored database or an ontology. It uses generative AI (Azure OpenAI Assistant APIs) to interpret the question, query the data and return an answer, and it can be published for other people, applications or agents to call. It is generally available and needs a paid F2 or higher Fabric capacity.
How is a Fabric Data Agent different from Power BI Copilot or a Copilot Studio agent?
Power BI Copilot works within a report and its semantic model; a Fabric Data Agent is a standalone, publishable agent that can span multiple sources — lakehouse, warehouse, semantic model, KQL — and be called by other apps and agents. Copilot Studio builds general-purpose conversational agents; a Fabric Data Agent is the data-grounded specialist you would plug into a Copilot Studio agent when it needs to answer from your governed Fabric data. In practice we often use them together.
What capacity and setup do we need to run one?
A paid F2 or higher Fabric capacity (or Power BI Premium per-capacity), the Fabric data agent tenant settings enabled, cross-geo processing for AI turned on where required, and at least one grounding source — a warehouse, lakehouse or Power BI semantic model. For application access rather than a person signing in, the agent supports service principals, so an app authenticates with its own identity.
How do you stop the agent giving wrong answers?
The honest answer is that the accuracy lives in the grounding, not the AI. We start with a governed semantic model where each measure has one definition, write source-specific instructions and example questions that steer the agent, and validate it against a set of test questions with known correct answers before it goes live. An agent on a clean, governed model is reliable; one on an ambiguous model will be confidently wrong, and no prompt fixes that.
Can it answer over both structured and unstructured data?
Yes — a data agent can reason over structured sources like a warehouse, lakehouse or semantic model, and unstructured content brought into the estate, and combine them in one answer. The practical constraint is that all of it needs to be reachable and governed within Fabric; scattered, ungoverned content produces unreliable answers. We unify and govern the sources first, then let the agent reason across them.
How do we keep it secure and compliant across regions?
The agent enforces the row-level security and sensitivity labels on the underlying data, so it respects the same boundaries as your governed reports — a user only gets answers for data they are allowed to see. We apply Microsoft Purview sensitivity and the OneLake catalog's governance so grounding sources are endorsed and classified, and configure cross-geo AI processing settings appropriately for GCC, UK, US and India tenants. Governance is part of the build, not a later hardening pass.
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