Organizations today store massive volumes of structured data sales figures, inventory records, and financial reports in Excel and CSV files across SharePoint libraries. When someone needs a quick insight from that data, the typical path involves downloading the file, opening it in Excel or Power BI, writing formulas or building visuals, and then sharing the result. It works, but it’s slow, manual, and requires a level of technical skill that not every team member has.

What if there was a way to simply ask a question and get the answer complete with accurate calculations and even a chart without ever leaving a chat window.

That’s the promise of Code Interpreter in Microsoft Copilot Studio. This preview feature allows Copilot Studio agents to go beyond simple Q&A by dynamically generating and executing Python code to analyze structured data files. When paired with a SharePoint Document Library as a knowledge source, it creates a powerful self-service analytics experience: users ask questions in plain English and the agent does the heavy lifting searching SharePoint for the right file, writing the code, running the computation, and delivering the result.

In this blog, we’ll explore what Code Interpreter is, how it works with SharePoint, and see it in action.

Code Interpreter is a capability within Microsoft Copilot Studio that enables AI agents to generate and execute Python code on the fly in response to user queries. Rather than relying solely on the large language model’s inherent reasoning which can be unreliable for math and data-heavy questions Code Interpreter offloads analytical tasks to deterministic Python computations.

The agent doesn’t estimate the answer. Instead, it writes a precise Python script, runs it against the actual data, and returns the calculated result. The math is real. The answer is accurate. And the user never sees a single line of code unless they choose to.

You can enable code interpret in the agent setting in Generative AI, scrolling down you will find the option for code interpreter and enable that.

There are two ways to feed structured data into a Copilot Studio agent for analysis. The first is user-uploaded files where someone attaches a CSV or Excel file directly in the chat. The second, and more enterprise-relevant approach, is connecting a SharePoint Document Library as a knowledge source. In this blog, we focus on the SharePoint approach.

When a user asks an analytical question, the agent follows a multi-step process behind the scenes:

1. Understand the query: The orchestrator interprets the natural language question and determines it requires data analysis.
2. Search SharePoint: The agent uses Work IQ Microsoft’s enhanced retrieval layer to search the connected SharePoint knowledge source and locate the relevant structured file.
3. Retrieve and inspect: The agent retrieves the file content and examines its structure columns, data types, and rows.
4. Generate Python code. Based on the query and the data, the agent writes a Python script tailored to answer the question.
5. Return the result. The output a table, a chart, a number, or a summary is delivered back to the user in the chat.

To demonstrate this capability, we have two structured datasets stored in a SharePoint Document Library SalesReport.xlsx containing employee-level sales performance data, and BusinessSalesAnalysis.xlsx containing order-level business data with products, categories, regions, and revenue.

With Code Interpreter enabled and the SharePoint files connected as a knowledge source, the agent can now answer analytical questions directly. Let’s see how it responds to a couple of queries.

Below are some of the question asked to the agent.

Query 1: “Which product category generated the highest revenue in the uploaded Excel report?”

The agent identified the relevant file, executed Python code to calculate revenue by product category, and returned a clear breakdown Electronics leading at $738,000 followed by Furniture at $517,000. It even highlighted key revenue drivers like Laptops and Monitors.

Query 2:  Analyze the uploaded sales dataset and provide a summary of overall business performance.

This time, the agent analyzed both datasets together and returned a comprehensive summary $1,255,000 in total revenue, $286,000 in profit, 321 units sold, and a 22.79% profit margin. It also generated a month-over-month revenue and profit trend table from January through May.

As you can see, the agent has used the Code Interpreter to answer the user’s queries generating Python code behind the scenes, running real calculations against the SharePoint data, and returning accurate, formatted results directly in the conversation. No manual data work. No formulas. Just a natural language question and a precise answer.

Conclusion

Code Interpreter in Microsoft Copilot Studio brings real computational power to the conversational AI experience. By combining natural language understanding with deterministic Python execution and grounding it in the structured files already living in SharePoint it creates a genuinely useful self-service analytics layer. Users ask questions in plain language. The agent finds the data, writes the code, runs the computation, and delivers the answer all within seconds.

FAQs

What is Code Interpreter in Microsoft Copilot Studio?

Code Interpreter is a capability in Microsoft Copilot Studio that enables AI agents to generate and execute Python code automatically. It allows agents to perform data analysis, calculations, visualizations, and file processing based on user queries, delivering accurate results directly within a conversation.

How does Code Interpreter work with SharePoint files?

When SharePoint Document Libraries are connected as a knowledge source, the Copilot Studio agent can locate relevant Excel or CSV files, analyze their contents, generate Python code to answer user questions, execute the code, and return insights such as summaries, calculations, tables, or charts.

Can Microsoft Copilot Studio analyze Excel files stored in SharePoint?

Yes. Microsoft Copilot Studio can analyze Excel files stored in SharePoint when the library is configured as a knowledge source and Code Interpreter is enabled. Users can ask questions in natural language, and the agent retrieves and analyzes the data automatically.

What types of files can Code Interpreter analyze?

Code Interpreter primarily supports structured data files such as Excel (.xlsx) and CSV (.csv) files. These files can be uploaded directly by users or accessed through connected SharePoint Document Libraries.

Why is Code Interpreter more accurate for data analysis?

Unlike traditional AI responses that rely on probabilistic reasoning, Code Interpreter generates and executes actual Python code against the source data. This ensures calculations, aggregations, and analytical results are based on real data processing rather than estimation.

The post Streamlining SharePoint File Analysis with Microsoft Copilot Studio Code Interpreter first appeared on Microsoft Dynamics 365 CRM Tips and Tricks.

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:

Column Name	Type
Employee Name	Text
Course Name	Text
Training Provider	Text
Cost	Currency
Training Date	Date
Approval Status	Choice (Pending, Approved, Rejected)

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.