Organizations frequently receive employee requests for training programs, certifications, or skill-development courses. Traditionally, these requests go through manual review and approval processes which can delay decision making and create administrative overhead.

With Advanced Approvals in Microsoft Copilot Studio, it is possible to automate such decisions using AI. Instead of relying on human approval stages, AI can evaluate the request details and decide whether the request should be approved or rejected based on predefined criteria.

In this article, we will build a Training Request Approval System where:

• A user creates a training request record in Dataverse
• An AI approval stage evaluates the request
• The AI automatically approves or rejects the request

This implementation demonstrates how AI-driven approvals can automate business decisions without human intervention.

Prerequisites

Before starting, ensure the following are available:

• Access to Microsoft Copilot Studio
• A Power Platform environment with Dataverse enabled
• Basic knowledge of Dataverse tables and Copilot Studio agent flows

Solution Overview

The workflow implemented in this article follows a simple structure.

1. Employee submits a training request
2. The request is stored in Dataverse
3. AI evaluates the request
4. The system updates the approval status

This removes the need for manual manager approvals and allows faster decision making.

Step 1: Create a Dataverse Table for Training Requests

First, create a Dataverse table that will store the training requests.

Example table: Training Requests

Suggested columns:

This table will be used by the AI flow to read and update request details.

Step 2: Create an Agent Flow in Copilot Studio

Navigate to Copilot Studio → Agent Flows and create a new flow.

Agent flows allow you to automate processes using AI and actions connected to data sources like Dataverse.

In this implementation, the agent flow will:

• Retrieve the training request
• Evaluate the request using AI
• Update the request status.

Agent Flow creation screen in Copilot Studio

Step 3: Configure the Multistage Approval Step

Add the Run a multistage approval action in the flow.

This feature allows AI to evaluate requests based on specific instructions.

Since this implementation focuses on AI-only approval, no manual stages are added.

The flow will only contain the Evaluate Request AI stage.

Run a multistage approval (preview) configuration screen

Step 4: Define AI Evaluation Instructions

Inside the Evaluate Request step, define clear instructions for the AI model so it knows how to evaluate the request.

Example instructions:

Evaluate the employee training request and decide whether it should be Approved or Rejected.

APPROVE the request if ALL of the following are true :

– The trainingCost <= 1000.00

– trainingStartDate is after the date the course is purchased (i.e. coursePurchaseDate)

– The request contains all required details including employeeName, courseName, trainingCost, and trainingStartDate.

REJECT the request if any of the above are false.

Note: Here trainingCost, trainingStartDate, employeeName, courseName, coursePurchaseDate are dynamic fields as shown below in the image

These instructions guide the AI model to consistently evaluate each training request.

AI instruction configuration inside the Evaluate Request stage

Step 5: Update the Dataverse Record

After the AI evaluates the request, configure the next step in the flow to update the Dataverse record.

Based on the AI decision:

• If Approved → Update Approval Status to Approved
• If Rejected → Update Approval Status to Rejected

This ensures the final decision is stored directly in Dataverse.

Dataverse Update Row action in the flow

Testing the AI Approval Process

Once the flow is configured:

1. Create a new training request record
2. Trigger the agent flow
3. Observe the AI evaluation
4. Verify that the Approval Status updates automatically

This demonstrates how AI can independently make approval decisions based on defined rules.

Dataverse Update Row action flow when all conditions are met / true

Dataverse Update Row action flow when all conditions are not met / false

Example training request record before and after AI evaluation

Challenges You May Encounter

While implementing AI approvals in Copilot Studio, you may encounter some configuration challenges.

1.Writing Effective AI Instructions

The AI model relies heavily on the instructions provided. If instructions are vague, the decision may be inconsistent.

To avoid this:

• Clearly define approval and rejection conditions
• Keep the logic simple and structured.

2.Mapping Dataverse Fields

Incorrect field mapping between the agent flow and Dataverse may prevent the AI from reading request data correctly.

Always verify:

• Column names
• Data types
• Input parameters passed to the AI stage.

3.Understanding AI Decision Outputs

The AI stage returns structured output which must be correctly interpreted when updating Dataverse records. Improper condition checks may cause incorrect status updates.

4.Preview Feature Limitations

The Multistage Approval feature is currently in preview, so some UI elements or configurations may change over time.

Benefits of AI-Driven Approvals

Implementing AI-based approvals provides several advantages:

• Faster decision making
• Reduced dependency on manual approvals
• Scalable automation
• Consistent evaluation logic
• Seamless integration with Dataverse

Conclusion

Advanced approvals in Microsoft Copilot Studio open new possibilities for automating decision-based workflows. In this example, we built a Training Request Approval System where AI evaluates each request and determines whether it should be approved or rejected.

FAQs: Automating Training Request Approvals with AI in Microsoft Copilot Studio

• What is AI-driven approval in Microsoft Copilot Studio?
  AI-driven approval in Microsoft Copilot Studio automates the decision-making process for requests, such as employee training requests. Instead of waiting for human manager approval, AI evaluates the request based on predefined rules and updates the approval status automatically in Dataverse.
• How does the training request approval system work?
  The system works in a few steps:

1. An employee submits a training request in Dataverse.
2. The AI agent flow in Copilot Studio retrieves the request.
3. The AI evaluates the request against predefined approval criteria.
4. The AI updates the request status as Approved or Rejected.

• What are the prerequisites for setting up AI approval in Copilot Studio?
  To implement AI-based training approvals, you need:

1. Access to Microsoft Copilot Studio.
2. A Power Platform environment with Dataverse enabled.
3. Basic knowledge of Dataverse tables and Copilot Studio agent flows.

• Can AI completely replace human approval for training requests?
  Yes, AI can handle approval entirely if the evaluation logic is clearly defined. AI ensures faster, consistent, and scalable approvals, reducing administrative overhead. However, organizations can still add manual review stages if needed.

The post Automating Training Request Approvals Using AI in Microsoft Copilot Studio first appeared on Microsoft Dynamics 365 CRM Tips and Tricks.

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.

The post How Copilot Studio Leverages Deep Reasoning for Intelligent Support Operations first appeared on Microsoft Dynamics 365 CRM Tips and Tricks.