Copilot Studio supports a range of knowledge sources out of the box and works well for many scenarios. As solutions grow, some teams reach a point where they want more control over how information is indexed, searched, and returned to an agent. This is where Azure AI Search becomes a useful extension.

In this session, we focus on the why and the how of using Azure AI Search with Copilot Studio. We start by discussing the situations where adding search provides clear benefits, such as large document collections, multiple data sources, or the need for more predictable retrieval. From there, we walk through how the integration works, what decisions matter during setup, and how those choices influence agent responses.

Rather than following a single setup recipe, the session highlights practical considerations like index design, data shaping, query behavior, and how Copilot Studio consumes search results. You will see how different choices affect accuracy, performance, and long-term maintainability.