Building an Autonomous AI Agent in Copilot Studio – Part 7: Add Tools to a Copilot Studio Agent

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Welcome to part 7 of this blog post series.

If you missed the previous parts, visit the links below:

In this part of this blog post series, I will connect a number of tools to my agent. Tools enable agents to automate processes and complete tasks without human intervention. There are over 1,000 pre-built connectors in the Power Platform, which allow us to connect to Microsoft and third party applications.

We can grant the agent access to the Excel files, Email, and more. Without tools, the agent would not have the permissions to access my data and make changes.

We need a combination of the trigger, the tools, and the instructions so the agent knows what is required of it. The end result is an agent that has access to the tools it needs, a trigger to start it, and clear instructions that guide how the tools should be used.

But haven’t we already added a trigger in an earlier post?
Correct, the trigger plays an important part. The trigger detects when a Microsoft Form is submitted and retrieves the responses. It allows the agent to capture details such as the course code submitted by the trainer.

However, for the agent to determine who to assign the request to, it also needs access to the central mentors Excel spreadsheet so it can compare the submitted course code with the central mentors.xlsx spreadsheet.

The agent therefore needs permission to read data from both the Microsoft Form and the Excel file. This is why we need to add tools. Tools allow the agent to access and work with external data sources such as Forms, Excel, and email.

In this post, we’ll explore the different types of tools available in Copilot Studio before adding the required tools to the Course Mentor Agent.

We will add and configure the following tools:

  • Get response details (Microsoft Form)
  • List rows present in a table
  • Add a row into a table
  • Send an email (V2)

Task: Add tools to agent

  1. Select tools
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2. Click + Add a tool

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You’ll find a number of tools to choose from. I have provided an explanation for each option in the table below.

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Tool optionWhat it doesExplanation
AllShows all available toolsDisplays every tool your agent can use in one place. Useful if you just want to browse everything that’s available.
ConnectorConnects to other servicesLets your agent connect to Microsoft or third‑party services (for example SharePoint, Dataverse, Outlook, or Teams) using built‑in connectors.
PromptAI‑based instructionAllows you to define a natural‑language prompt that the agent can run using AI, such as summarising text, rewriting content, or generating an explanation.
FlowRuns Power AutomateRuns a Power Automate flow from your agent, such as saving data to Excel, sending an automated email, or creating a ticket.
REST APICalls external APIsAllows the agent to call an external REST API directly to send or retrieve data from custom or third‑party systems.
Model Context Protocol (MCP)Connects to MCP serversMCP is an open‑source standard for connecting agents to external tools and data through an MCP server. When you connect to an MCP server, Copilot Studio automatically discovers the tools and data it provides (including names, inputs, and outputs). In many cases this feels plug‑and‑play as you don’t need to build anything yourself unless you want to expose your own systems. The MCP server provides the context and capabilities, and the agent uses them as needed.

There are over 1,000 tools available that allow an agent to connect to both Microsoft and third-party products to automate business processes. I’ve added a few images below that show the different types of tools available. This is just a small sample, there are many more available.

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Task: Add tool – Get response details (Microsoft Forms)



3. Search for and select Get response details, or use the search box if the tool is not visible.

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4. Click the Add and configure button, as shown in the image below

5. If a connection is not already established, click Create connection and sign-in

This will allow the agent to retrieve the Microsoft Form response, but we need to select the Microsoft Form created in part 1.

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6. Click additional details, as shown in the image below.

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7. I’ll be changing End user credentials to Maker-provided credentials, as shown in the image below.

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Why change this setting?

If I leave as the default End user credentials option enabled, users will be prompted to sign-in before the agent can retrieve form responses. This may work fine for internal only forms, but it can cause problems if the form is shared publicly, as not everyone will be able to sign-in. By switching to Maker‑provided credentials, the agent uses my connection to Microsoft Forms instead of the end user’s credentials. This allows the agent to retrieve form responses automatically, even when the form is available to external or anonymous users.

This setting depends on your scenario and how you want users to interact with your form.

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8. Next, scroll down to Inputs. This is where I select which Microsoft Form to use. I chose the form I created earlier in this blog post series (Part 1).

Click Dynamically fill with AI and select custom value

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9. Click inside the Unique identifier of the form field. A list of available Microsoft Forms will appear. I select the form I created earlier in part 1 of this series.

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10. The Response Id field will be left set to Dynamically fill with AI. This is because each time a trainer submits the form, a new and unique form response ID is generated, so it isn’t possible to provide a fixed value.

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11. Click Save

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Task: Add tool – List rows present in a table (Excel Online Business)


Next, I need to add an Excel tool so that my agent can access my Mentors Excel file, which includes a list of all course mentors.

12. Select Tools and search for Excel. Select List rows present in a table, as shown in the image below. This allows the agent to read all rows from the Mentors table and dynamically determine which mentors are qualified based on the course code is associated with them. The agent can then use this information later to select a suitable mentor for the request.

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13. If a connection is not already established, click Create new connection

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14. Click Create

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15. Sign-in

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16. Check that the connection is enabled.

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17. Again, as we did earlier with the Microsoft Forms tool, click the Add and configure button, as shown in the image below.

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18. Click Additional details and change the credentials to Maker-provided credentials, so the tools uses my credentials. Leave the name and description set to their defaults.

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19. Scroll down to Inputs

For Location, click the dropdown under Fill using, select Custom value. Click inside the Value box, and choose OneDrive for Business from the list.

For Document library, click the drop down under Fill using and select Custom value. Click directly inside the Value box, and select OneDrive from the list.

For File: Click and select the file. This would be my file named Mentors.xlsx

If you received the following error message: You are not authorized to use this connection. Please consider using a different connection. Manually type the name of the Excel file, such as Mentors.xlsx. This is common if you have only just created or uploaded the file.


20. Select Table1, or manually type Table1 if it does not appear.

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Note: the table name comes from Excel when your data is formatted as a table. With the table selected in your Excel file, go to Table Design at the top of the Excel spreadsheet. You’ll find the table name there, which usually defaults to Table1.

21. Click Save

22. You should now have two tools added to the agent

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Task: Add tool – Add a row into a table


23. We also need the agent to record the outcome of each mentoring request. This includes logging the requester’s details and the mentor who has been assigned.

To achieve this, we’ll add the Add a row into a table Excel tool, which will be used to add a row to the RequestLogs.xlsx file after a mentor has been selected.

In addition, we’ll add an email tool later in this post so the assigned mentor is notified when a new mentoring request has been assigned to them.

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24. Select Tools and choose Add a tool. Search for Add a row into a table (Excel Online Business), and select it from the list.

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25. Click Add and configure

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26. Click Additional details and change the authentication setting to Maker‑provided credentials, so the tool uses my account.

27. Click Inputs and configure the following settings:

Location: Select Custom value from the drop box, click in the Value box, and choose OneDrive for Business.

Document Library: Select Custom value from the dropdown, click inside the Value box and choose OneDrive.

File: Select Custom value from the dropdown, click inside the Value box, and browse to select file RequestLogs.xlsx.

Table: Select table1

Row: Leave this set to the default (if not already greyed out). We can’t specify a specific row, as new rows will be added dynamically by the agent.

Note: If you’re unable to browse to the file, manually type the name of the Excel file. This can happen if the file has not yet been indexed.

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28. Click save

Task: Add tool – Send an email


29. Select Tools, then click Add a tool. Search for and select Send an email V2 (Office 365 Outlook)

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30. If a connection is not already established, click Create connection and sign in

31. Click Add and configure

32. Click Additional details and change the authentication setting from End user credentials to Maker‑provided credentials, so the tool uses my account.

33. Scroll down to Inputs and configure the following:

To: Leave this field dynamic. The agent will determine who to send the email to by analysing the MentorEmail field in the Excel spreadsheet (Mentors.xlsx).

Subject: Select Custom value from the drop down and add a subject, Course Mentor Request

Body: Leave this field dynamic. The agent will generate a suitable email message based on the mentoring request.

That’s it, we’ve successfully added four tools, as shown in the image below.

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In the next post, we’ll return to the agent instructions we described in part 3 and make some amendments to instruct the agent to use the tools we’ve just created.

Click the following link to continue: Building an Autonomous AI Agent in Copilot Studio – Part 8: Amend Existing Agent Instructions to Use Tools – Cloud Build

Building an Autonomous AI Agent in Copilot Studio – Part 6: Create a Trigger in Copilot Studio

Reading Time: 4 minutes

Welcome to part 6 of this blog post series.

If you missed the previous parts, visit the links below:

In this blog post, I will create a trigger to detect when the Microsoft Form I created in part 1 is submitted by a trainer requesting a mentor to help with preparing for a course delivery.

What is a trigger in Copilot Studio?

A trigger in Copilot Studio defines when an agent should run.

Instead of waiting for a user to start a chat, a trigger allows the agent to run automatically when something happens. For example, when a Microsoft Form is submitted, a trigger can detect that event and start the agent without any user interaction.

Triggers are commonly used for autonomous agents, where the agent reacts to events such as:

  • A Microsoft Form being submitted
  • Data being created or updated
  • A scheduled or system event occurring
  • And many more

When the trigger fires, Copilot Studio passes the event information to the agent, and the agent responds according to the instructions it has been given.

Task: Create a trigger in the Course Mentor Agent to detect and process Microsoft Form submissions.

  1. Select the Overview tab and click Add trigger
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2. You will find a library of triggers from Power Automate which can be used in Copilot Studio, including:

  • When an item is created or modified in SharePoint
  • When a new response is submitted in Microsoft Forms
  • When a file is created/modified in OneDrive for Business
  • When a feed item is published in RSS
  • When an event is added to a calendar in Google Calendar
  • When a pull request is closed (Git) in Azure DevOps
  • When a task is completed in Planner
  • When a Teams messages is posted or if @mentioned
  • When a new email arrives
  • and many more

Explore the different triggers available. There were 843 triggers available at the time of writing this post.

3. I’ll be selecting When a new response is submitted (Microsoft Forms). Click Next

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4. The trigger will run using my currently signed‑in account. This can be changed if required.

This trigger uses the apps listed in the image below. A green check means the required connection is set up and ready to use.

Click Next

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5. Now that I’m connected to Microsoft Forms using my account, the form I created in Part 1 of this blog series is available to select. I select the form and click Create trigger.

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6. Click Close

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7. Select the Overview tab. You’ll see that the trigger is now visible.

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8. I test the trigger to confirm all is ok

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There is still further work to complete before we have a fully functioning autonomous agent. In the next post, I will add tools to my agent, so it has permissions to read the data inside the form, read my Mentors.xlsx file, add a row in my log file RequestLogs.xlsx, and send an email to the assigned mentor.

Click the following link to continue to part 7: Building an Autonomous AI Agent in Copilot Studio – Part 7: Add Tools to a Copilot Studio Agent – Cloud Build

Building an Autonomous AI Agent in Copilot Studio – Part 5: Create an Excel Log File to Record Mentor Requests

Reading Time: < 1 minute


Welcome to part 5 of this blog post series.

If you missed the previous parts, visit the links below:

In this blog post, I’ll create another Excel file and include the columns shown in the screenshot below. We will soon instruct the agent to update the spreadsheet when a form is submitted and a mentor is assigned. This log file will include the requester details and the mentor who was assigned.

I’ve named my Excel file RequestLogs.xlsx and store it in my OneDrive for Business.

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I now have two Excel files:

  • Mentors.xlsx (Created in Part 4)
  • RequestLogs.xlsx

Join me in the next post, where I will create a trigger in Copilot Studio to detect and retrieve information submitted via the Microsoft Form we created in part 1 of this blog post series.

Click the following link to continue to part 6: Building an Autonomous AI Agent in Copilot Studio – Part 6: Create a Trigger in Copilot Studio – Cloud Build

Building an Autonomous AI Agent in Copilot Studio – Part 4: Create an Excel Spreadsheet Listing Trainers and Qualified Courses

Reading Time: 2 minutes


Welcome to part 4 of this blog post series.

If you missed the previous parts, visit the links below:


In this blog post, I will create an Excel spreadsheet and store it in my OneDrive for Business account.

The Excel file will include a list of trainers who have volunteered to become mentors, assisting other trainers with course preparation. The agent will analyse the Microsoft Form submission, read the Excel spreadsheet, and automatically assign a suitable mentor based on the course selected. The Excel file could be kept in a SharePoint location in a production environment to allow for access and updates.

The Excel spreadsheet will include three columns including:

  1. MentorName
  2. MentorEmail
  3. Course

I name the Excel file mentors.xlsx and upload it to my OneDrive for Business. For the purpose of this demo, I have added the same email address for all demo users. Format the data as a table. By structuring the data as a table, the agent can cleanly filter and select the correct mentor.

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Ok, so that’s the Excel spreadsheet including the trainers.

In the next post, I will create an Excel spreadsheet where the agent will log details of the mentee and the mentor assignment.

Click the following link to continue to part 5: Building an Autonomous AI Agent in Copilot Studio – Part 5: Create an Excel Log File to Record Mentor Requests – Cloud Build

Building an Autonomous AI Agent in Copilot Studio – Part 3: Create a Copilot Studio Agent (Name, Image, Description, and Instructions)

Reading Time: 5 minutes


Welcome to part 3 of this blog post series.

If you missed the previous parts, visit the links below:


In this post, I’ll create an agent in Copilot Studio, give it a name, add an image, and define its description and instructions.

Task: Building an agent in Copilot Studio

  1. Access and log in to https://copilotstudio.microsoft.com
  2. My environment is set to Default. I’ll be switching from Default to Demo. To learn more about the different types of environments, visit Part 2: Create a Power Platform Environment
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3. Click Agents from the left pane.

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4. You can describe the agent in your own words to allow Copilot Studio to create your first draft for you. You could also use an existing template to give you a head start or create an agent from blank.

I’ll be creating an agent from blank by selecting Create blank agent top right corner of the Copilot Studio interface (as seen in the image below).

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5. The agent is provided with a default name, which we will change shortly. Wait for the following message to disappear, This feature isn’t available until your agent has finished setting up, as seen in the image below. This may take a couple of minutes. When completed, a message will appear to confirm Your agent has been provisioned.

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6. Now that the agent has been provisioned, I’ll go through and change some default settings first, starting with the name and Copilot agent image. Click edit.

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7. Change the agent name to Course Mentor Agent

I’ve changed the default image. To generate your own agent icons, visit Create | M365 Copilot.

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Description:
An autonomous mentoring agent for Imran Rashid Training Ltd that helps trainers request one‑to‑one mentoring when preparing to deliver Microsoft courses for the first time.
The agent processes mentoring requests submitted via a Microsoft Form, identifies the course the trainer needs support with (for example, AZ‑104, SC‑300, AZ‑700, AI‑102, AZ‑204, SC‑200, or SC‑100), and matches the request to a suitable mentor from a central mentor directory of experienced trainers who have volunteered to support others. Once a match is made, the agent notifies the assigned mentor via email so a one‑to‑one mentoring session can be arranged. The agent records the details to a central log file, which includes the requester details and the mentor that was assigned.

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8. Click Save

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9. Select your agent’s model. I’ll be leaving the default GPT-5-Chat model, which was in preview at the time of writing this post.

Explanation
The model selection controls which large language model your agent uses to reason, understand prompts, and generate responses. Different models are optimised for different styles of work, such as faster responses, deeper reasoning, or adapting between conversational responses and structured reasoning. Organisations can choose the model that best fits their scenario and can experiment with different models to see which one produces the most reliable and effective behaviour for their specific agent before standardising on a preferred option.

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10. Next, we add instructions for the agent to follow.

Agent instructions tell the agent how it should behave, what tasks it is responsible for, and what actions it should take when it runs. They provide the guidance the agent needs to work consistently and correctly.

Note: I’ll be updating these instructions later in this blog post series to include the specific details for the items shown in square brackets [ ].

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Instructions I entered are shown below.


You are the Course Mentor Agent for Imran Rashid Training Ltd. Your job is to analyse mentorship requests submitted by mentees and assign an appropriate mentor by following the instructions below.

Purpose
– Route mentoring requests from a Microsoft Form to an appropriate mentor.

Trigger:
1. When a new mentoring request is received via the form Trainer Mentoring Request, retrieve the submission details using [Get response details] and begin processing.

Data to capture from the form
2. Extract the following details from the form submission:
– Full Name
– Email Address
– Course Selection (course code)
– Country
– Specific Areas of Concern

Mentor lookup (mentors.xlsx)
3. Retrieve the mentor list from mentors.xlsx using [List rows present in a table]
– MentorName
– MentorEmail
– Course

Send an email to the assigned mentor
4. Send an email using [Send an email (V2)] addressed to the selected mentor. Format in html so it’s clear to read. Send the email after creating the draft email.

Example: Hi “MentorName”,
A mentorship request has been assigned to you for the course “Course Code”.
Requestor Name: “MenteeFullName”
Requestor Email: “MenteeEmail”
Country: “Country”
Thanks,
Course Mentor Agent at Imran Rashid Training

Log the request (RequestLogs / Requests table)
5.Use [Add a row into table] to dynamically complete the below:
Requester Name using the Full name field from the form
Email using the Email Address field from the form
Course using Course Selection from the form
Country using the Country field from the form
Concerns using the Specific Areas of Concern field from the form Assigned Mentor from the MentorName from the Mentors excel file


11. Click Save

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Thanks for following along. In the next blog post, I will create an Excel spreadsheet containing a list of trainers and the courses they have volunteered to support other trainers with through mentoring, and store it in my OneDrive for Business account.

Click the following link to continue to part 4: Building an Autonomous AI Agent in Copilot Studio – Part 4: Create an Excel Spreadsheet Listing Trainers and Qualified Courses – Cloud Build