Best Practices for Copilot Studio

As I’ve been developing AI-empowered automations at Encore, Microsoft Copilot Studio has been one of the top tools I’ve used. Along the way, I’ve learned some of the key dos and don’ts for efficiently using Copilot Studio.

So, in this blog post I’ve put together the best practices I and my colleagues have learned so far about using Copilot Studio to create AI agents and improve our productivity.

Table of Contents

• Introduction: What Is Copilot Studio?
• Go Beyond Copilot to Learn About Copilot Studio
• Design Agents That Bridge Multiple Microsoft Tools
• Leverage the Power of Copilot Studio Topics
• Break Up Your Agent Into Modular Topics
• Use Flow Within the Topic
• Follow the Developments With Dataverse MCP Server

What Is Copilot Studio?

At its core, Copilot Studio lets you build agents—think of them as smart assistants—that can connect to your other Microsoft applications and help automate tasks to boost productivity. Get a primer on the difference between Copilot and Agents.

The main difference between Copilot Studio and regular Copilot is that normal Copilot is designed for general productivity, whereas Copilot Studio is a specialized tool for creating custom AI agents and designing automations that connect to other Microsoft applications and services. The difference between Copilot Studio and Power Automate, which you may also be familiar with, is that Power Automate is specifically an automation tool that works best with repeatable processes – and Power Automate can be triggered within Copilot Studio’s “Topics” functionality.

In my experience, the main benefit of using Copilot Studio is that it includes generative AI functionality and can be used to design specialized agents that include structured automations that connect to different Microsoft business applications and services like Teams, Outlook, Azure DevOps, and Dynamics 365.

Copilot Studio is designed to be easy to get started with, but to really unlock its full power, you’ll need some experience with the broader Microsoft stack. That’s because making automations that truly benefit your business often requires knowing how to pass outputs between different applications, using things like JSON or Power Automate Flows. So Copilot Studio is not really a tool that beginners can use successfully from end to end – you’ll need an experienced developer for most use cases.

Go Beyond Copilot to Learn About Copilot Studio

In my experience, relying on Copilot itself to teach you about Copilot Studio can get you started, but cannot give you the information you need to make agents that are ready to deploy and increase efficiency in your business.

An AI tool like Copilot or Gemini, when you ask it for information about developing automations like these, will just give you general instruction. You’re going to need to rely on your professional experience, research, and some trial-and-error, to get your agent fully functional and deliver business value.

In other words, vibe coding can help you get started with Copilot Studio, but don’t expect it to carry you all the way in one shot.

Design Agents That Bridge Multiple Microsoft Tools

One of the main advantages of Copilot Studio is the ability to connect AI functionality to the other sources of data and automations in your other Microsoft applications and services. So, don’t limit your thinking to just “what can I do with Copilot?” Instead, think about “what can I do with Copilot Studio – connected to my CRM – connected to my inbox – connected to Flow,” and so on.

I’ve used Copilot Studio to create automations that transfer info about Teams meeting attendees to Dynamics 365 CRM records, and to turn unstructured information about user needs into Azure DevOps requirements and test cases.

Put in Effort to Prepare Outputs for Transfer Between Systems

However, when you’re doing those connections, you are going to need to build automations to make the outputs from one application fit properly in the other.

Here’s an example of the kind of “massaging” you’ll need to do. In one case, I had Copilot Studio importing Copilot natural language output about user needs into DevOps, but DevOps cut off every sentence of description as soon as it reached the first comma. (If you’ve worked with CSV files before, you know why that is.)

So I ended up having to reformat the output to use the “Pipe” separator, so DevOps could read the whole entry for each item.

AI and Copilot Studio are very powerful, and take care of many tasks amazingly quickly. But expect to be spending a significant amount of time testing out specific tweaks to the automation so outputs from one system can be ingested by the next.

To give you a sense of what that work looks like, here’s a screenshot of part of my Power Automate Flow inside my Copilot Topic. This is for preparing the output from the Agent for ingestion by the next application.

This shows all the preparation I did for the file. I had to put carriage returns with the “newLine” step, split the output in the “splitByLines” step, and so on. I had to do trial and error here to get the right format that I needed. Expect to spend some time on this kind of file preparation as you design your Copilot Agents.

Leverage the Power of Copilot Studio Topics

Within Copilot Studio, there are many options, but currently one of the most useful is Topics. A Copilot Studio Topic is one of the core building blocks used to design the “conversational” logic in Microsoft Copilot Studio.

Copilot Studio Topics are useful because they make your agent modular, scalable, and easy to maintain. Copilot listens to natural language and picks the correct topic based on keywords and trigger phrases.

Topics are very powerful and you can use them to do much more with your agents. Until recently, most of our staff would only use the “knowledge” section for the agent, and that works essentially like you’re doing a Google or Bing search over the knowledge base that you’ve added. That’s helpful, but Topics can take you beyond that basic functionality.

The main difference between a Topic and a knowledge source is that in a Topic, you can perform tasks, collect inputs and run business logic. Whereas in using a knowledge source, you only retrieve passive information. A knowledge source is like a content repository that your Copilot can read from to answer informational questions.

Learn more about Copilot Studio Topics.

Break Up Your Agent Into Modular Topics

In a recent build, I put all of the functionality I needed in one big Topic that included Copilot transcript analysis, Flows to massage the output, uploading to DevOps, etc.

Afterward, I realized I should have divided the functionality into multiple Topics, which would make it a lot easier for myself and the rest of the team to maintain and edit.

Developers should use modular topics whenever logic repeats, a flow gets too long or too complicated, or a task naturally breaks into smaller steps. They keep Copilot clean and easier to maintain by allowing multiple topics to call the same logic.

Use Flow Within the Topic

Power Automate Flow can be leveraged within Topics to enhance agent functionality, enabling a wider range of automations and connections between applications in the Microsoft ecosystem.

Flows inside Topics are useful for automating actions that the conversational Copilot itself cannot perform on its own, e.g., creating records, retrieving data, triggering processes, and data parsing. It extends a Topic’s capabilities so the Copilot can move from just answering to actually doing something.

Follow the Developments With Dataverse MCP Server

A major area to watch for new Copilot Studio functionality will be the Dataverse MCP Server.

MCP stands for Model Context Protocol, and it’s a way for Copilot Studio to access data from external sources, in this case Dynamics 365.

With Dataverse MCP in Copilot, you can now query, read, and update Dataverse data directly through “natural language”, without building custom connectors or Power Automate flows. It enables Copilot to list tables, describe schemas, run CRUD operations, and interpret multi‑table queries using built‑in Dataverse tools.

For now, the Dataverse MCP is still fairly simple and limited, but I expect to see more Microsoft development in this area for better integration between Copilot Studio and Dynamics 365 CRM, Business Central, and Finance & Operations.

Turning Experimentation Into Impact With Copilot Studio

Copilot Studio is a relatively new tool, but it’s already very powerful. The teams that will get the most value from it will be those that combine hands‑on experimentation with expertise across the broader Microsoft stack and an understanding of how their teams actually work day to day.

For related information, check out Copilot vs Agents: What’s the Difference and How They Work Together, our guide to Building Copilot Studio Chatbots That Work with Your Microsoft Stack, and our general Microsoft Copilot page to see how these tools fit into the full Microsoft ecosystem.