Microsoft Copilot Studio : Future of AI Agents [2026]

In Microsoft Copilot Studio, System topics are built-in conversation topics that handle common, essential behaviors of a copilot.

we don’t create them from scratch—they come preconfigured so that our copilot can work smoothly out of the box.

system topics are provided out-of-the-box to ensure the agent behaves predictably during errors, handoffs, or start-up.

In Microsoft Copilot Studio, Topics are the discrete conversational paths that define how our agent interacts with a user.

In our previous tutorial, we have learned about system Topics.

System topics are built-in conversation topics that handle common, essential behaviors of a copilot. we don’t create them from scratch—they come preconfigured so that our copilot can work smoothly out of the box.

In this session, we will learn a step-by-step guide for creating Custom Topics manually in Microsoft Copilot Studio theoretically and practically

In Copilot Studio, Topic Triggers defines when a topic starts.

Topic Triggers are the rules to tell our bot when to start a specific conversation flow / Topic ,when can be activated by user input like trigger phrases ("book a demo"), events, or even agent actions, by allowing the bot to understand intent and initiate a relevant, step-by-step interaction for a particular task efficiently.

In this session, we will learn about, branching a topic in a copilot studio.

First we will understand What Does “Branching a Topic” Mean?

In Copilot Studio, Branching means creating different conversation paths inside the same topic.

Branching is the process of creating "If/Then" logic to send users , different paths based on their:

• User responses

• Variable values

• Conditions (Yes/No, matches, comparisons)

• Business logic

In simple words:

Branching lets our copilot decide “what to do next” based on user input.

In Microsoft Copilot studio, During a conversation with an agent, it triggers the appropriate topic based on the user's input.

System Fallback is a system topic that is triggered when the Copilot agent cannot understand the user’s intent or match the user query with any existing topic.

In Microsoft Copilot Studio, an Unknown Intent is used when the agent cannot understand the user’s request or the request does not match any existing topic or intent. It acts as a fallback response to guide the user.

Escalate topic is a system topic in Copilot Studio, that is used to transfer the conversation from the AI agent to a human agent or external support system when the bot cannot resolve the user’s request.

It ensures that users do not get stuck with the bot when their issue requires human assistance.

In this session we will understand and learn about entities in Copilot studio.

Agents involves defining types of information (like dates, names, or custom product IDs) to extract from user input, making conversations dynamic by using built-in types or creating custom ones and then applying them in Topic nodes (Question nodes) to capture and use specific data for personalized interactions and branching logic, effectively teaching your bot to understand real-world subjects

Entities and variables help our agent to understand user input, store information, and reuse data across conversations and topics.

In our previous tutorial, we understood what is an entity?

And we've covered the different Types of Entities theoretically.

Now in this session, we will learn how to work with prebuilt entities practically.

Prebuilt entities are the "out-of-the-box" intelligence ,

which means Prebuilt entities are ready to use immediately in copilot.

Prebuilt entities, allow our copilot, to recognize, extract, and normalize common data types from user input without defining every possible variation

In Copilot Studio , Regex entities are custom entities that use regular expressions (RegEx patterns) to capture and validate specific types of user inputs

Regular expression (regex) entities let us to define logical patterns that we can use to match and extract information from an input. They allow us to define precise rules for recognizing data that doesn’t fit into prebuilt categories.

Regex entities are great for complex pattern matching against a user's input, or if you need to allow for specific variations in how a user might format or enter their input in a conversation.

In Microsoft Copilot Studio, variables are used to store and remember information during a conversation.

When a user provides input—such as their name, email, or order number—the copilot saves it in a variable.

variables are used to store information provided by a user or the system, allowing the copilot to create personalized conversation flows, reuse information across topics, and build conditional logic.

Alright,

In Copilot studio, we are Having 2 main concepts regarding variables

One is Variable Scopes: The variable scope determines which topics can access and modify the variable.

Second one Variable Data Types: where as data types define What kind of data will be stored in the variable.

In Microsoft Copilot Studio, topic variables are used to store information that is only needed within a single topic.

• Global variables in Microsoft Copilot Studio store information that is available across all topics during a conversation.

• Unlike topic variables, they are not limited to a single topic.

• Global variables help the copilot remember important details such as user name, email, customer ID, or selected service.

• we usually set a global variable when you first collect information from the user or receive data from another topic or Power Automate flow.

• Once set, the same value can be reused in different topics without asking again.

• This improves conversation flow, reduces repetition, and creates a more personalized, seamless user experience across the copilot.

In Copilot Studio, Connector actions allow our copilot to interact with external systems such as Dataverse, SharePoint, Outlook, Dynamics 365, SQL, or custom APIs.

Connector actions are powered by Power Platform connectors and are used to perform real business operations during a conversation.

we can add a connector action inside a topic to retrieve data, create records, update information, or trigger workflows.

In Copilot Studio, these tools are known as Actions—previously called them as plugin actions.

Tools are used to extend the copilot's capabilities by integrating with external services like Power Automate flows, connectors, REST APIs, and low-code plugins.

They enable the copilot to connect with enterprise systems, trigger workflows, retrieve data, and perform real business operations during a conversation.

When a user asks a question like, “Check my bank balance” or “Create a new bank account,” the copilot doesn’t guess the answer.

Before working with tools, first we will understand why we need to work with tools?

We need to work with tools, to transform a copilot from a conversation-only assistant into an action-driven AI agent.

Without tools, a copilot can only provide static or generic answers.

With tools, it becomes an intelligent, enterprise-ready AI agent that can interact with data, systems, and business processes

it can connect to enterprise systems, fetch real-time data, perform business operations, and automate workflows.

Tools play an important role in agentic AI, where copilots can plan, reason, and decide which action to take next.

“cr32f_accountnumber eq “ & Global.Var_accountNum

Azure Logic Apps, Microsoft Power Automate, Microsoft Power Apps, and Microsoft Copilot Studio offer over 1,000 + prebuilt connectors to connect to Microsoft and verified services.

In our previous tutorial, we have create a tool by interacting with Dataverse.

In Copilot Studio, copilots or agents often need to interact with systems outside Microsoft’s standard connectors.

This is where custom connectors come in.

Custom connectors allow a copilot to securely connect to external or internal APIs that are not available out of the box.

A custom connector wraps a REST API into a reusable, secure connector that can be used across Copilot Studio, Power Automate, and Power Apps.

It defines how the copilot authenticates, what operations are available, and what inputs and outputs are expected.

Once created, these operations appear as Actions (Tools) that a copilot can call intelligently.

Custom connectors are especially valuable in enterprise environments.

Many organizations rely on legacy systems or in-house applications, and custom connectors make it possible to integrate those systems directly into a copilot experience.

For example, a banking copilot can use a custom connector to check loan status from a core banking system, or an HR copilot can retrieve employee information from an internal HR platform.

Security and governance are key advantages of custom connectors.

They support multiple authentication methods, respect enterprise policies, and ensure copilots access data safely and responsibly with controlled access to sensitive data

A bank wants to build a Customer Support Copilot that allows customers to check their account balance and loan status by chatting with the copilot.

The bank already has a core banking system API, but there is no prebuilt connector available. So, we create a Custom Connector.

A custom connector is a wrapper around a REST API that allows Logic Apps, Power Automate, or Power Apps to communicate with REST or SOAP API.

Custom connectors allow us to create (and even share) a connector with its own triggers and actions.

we will learn 2 concepts

1) How to add the custom connector as a tool in our Copilot Studio agent in simple steps

2) How to call the tool “Macro bank” in custom topic

Model Context Protocol is a standardized way for Copilot to understand what tools, data, and capabilities are available, and how to use them correctly.

Model Context Protocol (MCP) allows users to connect with existing knowledge servers and data sources directly within Copilot Studio.

· API stands for Application Programming Interfaces, which is a traditional way for systems to communicate.

In Copilot Studio, when we use an API, we typically connect through a custom connector or REST endpoint.

· MCP (Model Context Protocol) is a standardized protocol that lets Copilot Studio agents dynamically discover and invoke external tools, data sources, and knowledge servers at runtime. It’s essentially a universal way to expose functions and data to an AI agent so the agent can use them intelligently.

Adaptive Cards are JSON-based UI definitions that allow us to design and render interactive content such as:

• Forms (input fields, dropdowns)

• Notifications

• Approval workflows

• Dashboards

They automatically adapt to the host app’s look and feel.