Decoding the Answer: How Copilot Studio’s Gen AI Orchestration Handles Complex Policy Queries

Intro:
When building copilots using Microsoft Copilot Studio, it's common to connect them to a SharePoint knowledge base filled with internal documentation—like company policies, procedures, and guidelines. However, as the knowledge base grows, so does the complexity.

Documents may overlap in content, especially when policies vary by region or department. For example, a travel expense policy might have different rules for employees in the UK versus those in the US, all stored in separate but similarly titled documents. I found myself wondering: How exactly did the copilot decide which document to use when answering my question? Copilot Studio’s powerful preview features—reasoning models—which offer a behind-the-scenes look at how Gen AI orchestration interprets and generates responses.

Setup:

• To simulate a real-world enterprise scenario, I built a Copilot Studio chatbot designed to help employees navigate internal policy documents—such as travel expenses, holiday entitlements, and other HR guidelines—stored in a SharePoint knowledge base.

• The instruction component is described to start by detecting the user’s region, filters documents strictly to match that region, and selects only one relevant document per topic—falling back to global content only when necessary. Responses are formatted using bullet points, always include source citations, and maintain a friendly, helpful tone with consistent greetings and closings. This setup ensures clarity, accuracy, and a positive user experience while keeping the copilot focused on its core purpose.

• Enable Gen AI orchestration

• enable the "Use deep reasoning models" in the setting

Testing:

So trying to explore the reasoning model

Remarks:

Instead of embedding reasoning into every interaction by hardcoding it into the agent’s instructions, I chose to explore the reasoning model as a conversational tool—using it selectively to understand how the copilot arrived at its answers. This lightweight, on-demand approach gave me a first glimpse into the orchestration layer of Copilot Studio, and I must say, it’s quite impressive. It opens up exciting possibilities for transparency, debugging, and even user trust. I’m looking forward to seeing how this feature evolves and how it can be more deeply integrated into future copilot experiences.

Reasoning Model