Building Reliable Enterprise Data Agents

An enterprise data agent should not be judged by its best demo answer. It should be judged by what happens when an ordinary user asks an ordinary business question, with imperfect wording, incomplete context, and real security constraints.

Think of the agent as a new analyst joining your team. Giving that analyst a chat window does not make them reliable. They still need trusted sources, clear definitions, approved calculation methods, access boundaries, tests, and someone accountable for the result.

That is how we will build this course. You will begin with the data foundation rather than the chat interface. You will define the questions the agent must answer, prepare the schemas and business language behind those questions, build a Fabric Data Agent, inspect the SQL, KQL, and DAX it generates, and evaluate it against a small but meaningful test set.

Then we will extend the architecture. A Fabric Data Agent is the analytical specialist. A Microsoft Foundry agent is the broader orchestrator. You will learn how they can work together through the preview Foundry integration, with the Fabric tool using the end user’s identity while Foundry coordinates data, documents, tools, and controlled actions.

The final lesson turns the architecture into a production operating model, covering governance, security testing, observability, release discipline, and conversation analytics.

Work through the lessons in order and complete the exercise in each one. Reliability is built layer by layer. Skipping the foundation usually means returning later to repair it, when the system is already harder to change.