Fabric Data Agent in London
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.
A Fabric Data Agent is a virtual analyst that answers plain-English questions over your data. It is only as trustworthy as the semantic model and governance underneath it — which is the part most demos skip. UK manufacturing clients are typically further along in data maturity than GCC or India counterparts — Power BI is more embedded, Azure adoption is more advanced, and data literacy in the operations team is higher. The gap is usually not the foundation — it's the intelligence layer. Predictive maintenance, demand sensing, automated exception management. The data is there. The models that act on it aren't.
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Rules of origin & supply chain compliance analytics
UK-based pricing available
UK data trail and compliance experience
UK client delivery fully remote-capable
What we hear from operators
The problems we solve
These aren't hypothetical pain points assembled from industry reports. They're observations from actual plant floors, warehouse ops, and finance desks — written down because they come up in almost every first conversation.
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.
How we work
Our approach
01
Ground the model before the agent
We start with the semantic model and the data it reads — clean measures, one definition of each KPI, the sources unified in OneLake. An agent is a reasoning layer over that foundation, so we get the foundation trustworthy first. This is the step that separates an agent people rely on from one they quietly stop using.
02
Build, instruct and test the agent
We configure the Fabric Data Agent across the right sources, write the source-specific instructions and example questions that keep it accurate, and validate it against a set of test questions with known correct answers before anyone trusts it. The agent uses Azure OpenAI Assistant APIs under the hood; the accuracy comes from the grounding and the instructions we give it.
03
Govern, secure and publish
We enforce row-level security and sensitivity so the agent respects the same boundaries as your reports, set up service-principal access for applications, and publish the agent so people, Copilot and other agents can call it. Then we monitor which questions it answers well and where it needs refinement, because an agent is maintained, not finished.
What changes
Outcomes
These are specific, measurable shifts — not benefit statements. Every outcome listed here has been achieved with a client.
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.
Technology stack
Common questions
What buyers ask us
These are questions that come up in almost every first or second conversation. If yours isn't here, it will be in the first call.
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.
Further Reading
Practitioner insights on this topic
Other markets
Fabric Data Agent in other markets
The operational problem rarely changes at the border. The ERP estate, the compliance regime and the reporting cycle do.
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First call is 30 minutes with Amit. We ask about your systems, your team, and your most pressing operational problem. You get a clear view of where the gap is and what closing it looks like. No slides. No pitch deck. No obligation to proceed.