Introduction
With the rise of AI copilots and agents, there’s a growing demand for tools that allow developers and domain experts to build autonomous systems quickly and effectively. Copilot Studio, part of the Microsoft Power Platform, allows you to create, extend, and customize AI copilots—AI agents capable of decision-making, interacting with users, and executing workflows.
In this post, we’ll walk through how to build a task-oriented autonomous AI agent using Copilot Studio with Power Fx, plugins, external APIs, and memory management.
🧠 What is an Autonomous AI Agent?
An autonomous AI agent is an entity that:
- Perceives its environment
- Decides based on context and rules
- Acts by interacting with users or systems
- Learns or adapts using memory or feedback
Copilot Studio provides a low-code + pro-code environment to bring this to life using:
- Conversational flows (topics)
- Plugins (to call external systems)
- Memory and variables
- Actions (Power Automate / API calls)
- Conditions and branching logic
🛠 Prerequisites
To follow along:
- You need access to Copilot Studio (via https://copilotstudio.microsoft.com).
- A Microsoft Dataverse environment (optional but recommended for persistent memory).
- Power Automate access for custom workflows.
- Basic familiarity with Power Fx.
🧭 Step 1: Define the Agent’s Purpose
Let’s build an agent that schedules meetings autonomously using Microsoft 365 Calendar APIs.
The agent should:
- Greet the user
- Ask for preferred meeting time
- Check the user’s availability
- Book the meeting and send invites
💬 Step 2: Create a Custom Topic
In Copilot Studio, create a new topic:
Trigger Phrases:
Schedule a meeting
Book a call
Set up a calendar event
Add Conversation Nodes:
Greeting Node:
Hello! I'd be happy to help schedule a meeting. When would you like to meet?
Store the user response in a variable:
Set(meetingTime, UserInput)
📞 Step 3: Check Availability Using Plugin or API
Use a plugin to call Microsoft Graph API to check calendar availability. Here’s how to integrate:
Power Automate Flow (plugin):
Create a flow: CheckAvailability
- Trigger: HTTP (from plugin)
- Action: HTTP request to:
GET https://graph.microsoft.com/v1.0/me/calendarview?startDateTime=...&endDateTime=...
Return availability data.
Plugin JSON Example:
{
"name": "CheckUserAvailability",
"description": "Checks if the user is free at a specified time",
"parameters": [
{ "name": "startTime", "type": "String" },
{ "name": "endTime", "type": "String" }
],
"response": {
"type": "boolean",
"name": "isAvailable"
}
}
Call the plugin in conversation:
Set(isFree, CallPlugin("CheckUserAvailability", {startTime: meetingTime, endTime: DateAdd(meetingTime, 30, Minutes)}).isAvailable)
Add branching logic:
If(isFree,
"Great! You're free at that time. I'll schedule it now.",
"Oops, you're already busy. Would you like to try another time?")
📅 Step 4: Book the Meeting
Use another plugin or Power Automate Flow:
Flow: CreateMeeting
- Input: meetingTime, attendees, subject
- Call Graph API:
POST https://graph.microsoft.com/v1.0/me/events
Authorization: Bearer <token>
Content-Type: application/json
{
"subject": "AI Meeting",
"start": {
"dateTime": "<meetingTime>",
"timeZone": "UTC"
},
"end": {
"dateTime": "<endTime>",
"timeZone": "UTC"
},
"attendees": [
{ "emailAddress": { "address": "someone@example.com", "name": "Someone" }, "type": "required" }
]
}
Call this from the copilot:
Set(result, CallPlugin("CreateMeeting", {startTime: meetingTime, endTime: DateAdd(meetingTime, 30, Minutes), subject: "AI Meeting"}))
Then show confirmation:
✅ Your meeting has been scheduled for {meetingTime}. Invitations have been sent.
🧠 Step 5: Use Memory and Context
Make the agent context-aware by storing variables like user’s timezone, preferred duration, and email.
Example:
Set(userTimeZone, "UTC")
Set(preferredDuration, 30)
Persist across conversations using long-term memory:
SetPersistentVariable("preferredDuration", 30)
And later:
GetPersistentVariable("preferredDuration")
🧪 Step 6: Add Testing and Failover
Use the test bot in Copilot Studio to validate the flow. Add fallback nodes for error handling:
Hmm, something went wrong. Can you try again or give me a different time?
🧩 Bonus: Let the Agent Reason
You can even add Power Fx-based reasoning, for example:
If(Hour(meetingTime) >= 18,
"You seem to be trying to schedule an evening meeting. Shall I find an earlier slot?")
Or guide the user with alternatives.
✅ Summary
In this post, we built an autonomous AI agent using Copilot Studio. We covered:
- Building a conversational flow using topics
- Calling external APIs with plugins
- Managing memory with Power Fx
- Using Power Automate to orchestrate external logic
- Adding logic and reasoning to make the agent adaptive
This approach can be extended to domains like customer support, sales automation, IT ticketing, or even internal copilots for employees.
🧠 Next Steps:
- Integrate adaptive cards in Teams for richer interactions.
- Store user profiles in Dataverse for personalization.
- Extend with Azure OpenAI for generative responses.
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