TL;DR: Deep Reasoning in Microsoft Copilot Studio enables AI agents to analyze multi-step support scenarios, evaluate historical case data, apply business rules, and recommend well-reasoned actions similar to how an experienced support specialist thinks.
AI agents are becoming a core part of customer service operations, but traditional conversational models often struggle when scenarios become complex, like diagnosing a multi-step issue, understanding multi-turn case histories, or recommending the next best action.
Microsoft’s new Deep Reasoning capability in Copilot Studio (currently in preview) bridges this gap by enabling agents to think more logically and deliver more accurate conclusions.
This feature equips Copilot agents with advanced analytical abilities similar to how a skilled support specialist breaks down a problem, evaluates evidence, and suggests well-reasoned actions.
How Deep Reasoning Works
Deep reasoning is powered by an advanced Azure OpenAI model (o3), optimized for:
- Multi-step thinking
- Logical deduction
- Complex problem solving
- Chain-of-thought analysis
- Context comprehension across long conversations
When enabled, the agent automatically decides when to invoke the deep reasoning model, especially during:
- Complicated queries
- Multi-turn conversations
- Tasks requiring decision making
- Summaries of large case files
- Applying business rules
Alternatively, you can instruct the agent to explicitly use deep reasoning by including the keyword “reason” in your agent instructions.
Business Use Case:
Imagine a company that manages thousands of service cases, technical issues, warranty requests, customer complaints, and product inquiries.
Handling these efficiently requires deep understanding of:
- Historical case data
- Case descriptions across multiple interactions
- Dependencies (products, warranties, previous repairs, SLAs)
- Business rules
- Customer communication patterns
A standard AI model can answer simple questions, but when a customer or sales representative asks something like:
- Why was this customer’s case reopened three times?
- Given the reported symptoms and past activity, what should be the next troubleshooting step?
- Which SLA should be applied in this situation, and what is the reasoning behind it?
- Considering the notes from all three departments, what appears to be the underlying root cause?
Your agent needs more than a direct lookup.
It needs reasoning.
This is where Deep Reasoning dramatically improves the experience.
How to Enable Deep Reasoning in Copilot Studio (Step-by-Step)
Setting up deep reasoning in a Copilot Studio agent is straightforward:
Step 1. Enable generative orchestration
This allows the agent to decide intelligently which model should handle each part of the conversation.
Step 2. Turn on Deep Reasoning
When enabled, the o3 model is added to the agent’s orchestration pipeline.
Step 3. Add the reason keyword (optional but recommended)
Inside the Agent Instructions, specify where deep reasoning should be applied:
As mentioned in the screenshot below, the word “reason” is used twice to trigger deep reasoning in our custom agent.
Step 4. Connect data sources
You can link multiple sources such as:
- Dataverse Cases table
- Knowledge bases
- SharePoint documents
- Product manuals
- Troubleshooting guides
Deep reasoning enables the agent to interpret and analyze these materials more effectively.
For this example, I connected a Dataverse MCP server to provide the agent with improved access to Dataverse tables.
Step 5. Test complex scenarios
Ask real-world questions like:
- Analyze the case history and determine the most likely root cause.
- Based on the customer’s issue description, what steps should the technician take next?
- Explain why this case breached SLA.
You will notice the agent provides a structured, logical answer rather than surface-level information.
You can also verify that deep reasoning was activated by checking the Activity section.
Frequently Asked Questions About Deep Reasoning in Copilot Studio
What model powers Deep Reasoning in Copilot Studio?
Deep Reasoning is powered by the Azure OpenAI o3 reasoning model, optimized for multi-step analysis and logical deduction.
When should Deep Reasoning be used?
It should be applied to complex, multi-turn conversations involving business rules, SLAs, historical data, or decision-making.
Does Deep Reasoning replace standard Copilot responses?
No. Copilot Studio dynamically decides when Deep Reasoning is required, using standard models for simpler interactions.
Can Deep Reasoning analyze large case histories?
Yes. It is specifically designed to interpret long conversations and large volumes of contextual data.
Conclusion
By connecting rich data sources and enabling deep reasoning, the agent becomes significantly more capable of understanding complex case scenarios and providing meaningful, actionable responses. When tested with real-world questions, the agent demonstrates structured analysis, logical decision-making, and deeper insights rather than surface-level replies.
This ensures more accurate case resolutions, improved productivity, and a smarter, more reliable support experience.