How to Create and Use AI Agents in GitHub Copilot

July 27, 2026

GitHub Copilot agents are autonomous sessions designed to handle multi-step tasks from end to end, such as implementing features or refactoring code. You direct these agents through chat interfaces, providing them with tasks and context, and they use a variety of tools to interact with your codebase, run commands, and iterate until the job is done.

Understanding AI Agents in Copilot

AI agents in Copilot are powered by language models and utilize various tools to interact with your development environment and external services. They are distinct from simpler AI features like inline suggestions, which use specialized completion models without an agent loop or tools.

Core Concepts of Copilot Agents

To effectively create and utilize AI agents in Copilot, it's crucial to understand the underlying concepts that work together in an agentic loop:

Language Models: These are the AI models that power all Copilot features, including agents. The paid tier of GitHub Copilot allows for the use of multiple advanced models.
Context: VS Code assembles information for the model, including open files, your selection, and conversation history. This context is critical for the agent to understand the task and perform relevant operations.
Tools: These are mechanisms that allow agents to act on your development environment. They enable agents to perform tasks like reading files, executing coordinated changes across multiple files, and running terminal commands.
Agents: This concept encompasses the agent loop, different agent types, subagents, memory, and planning. The agent uses the provided context and its tools to plan and execute steps, iterating and even self-correcting based on outcomes like test failures.

Agent Capabilities and Workflow

The paid tier of GitHub Copilot unlocks the full potential of agentic workflows, enabling comprehensive task execution. Agents can:

Manage Multi-step Tasks: They can handle complex workflows that require multiple steps, such as implementing a new feature specified in a GitHub issue or performing a large-scale refactor.
Interact with Files: Agents can read and modify multiple files across your project, enabling coordinated changes that would be tedious to perform manually.
Execute Commands: By running terminal commands, agents can integrate with your build process, run tests, and interact with version control.
Iterate and Self-Correct: A key feature of the agent loop is its ability to iterate. If a test fails after a code change, the agent can analyze the error and attempt to fix it, re-running the test to confirm success.

The primary interface for this workflow is Copilot Chat, which supports multi-turn conversations for assigning tasks, asking questions, and exploring ideas. For quick, focused edits, an inline chat option is also available directly in the editor.

How to Create and Use Copilot Agents

Creating and utilizing AI agents in Copilot primarily involves leveraging its integrated features within VS Code. While there isn't a "create new agent" button for building one from scratch, you configure and direct existing Copilot agent functionalities to perform tasks on your behalf.

Practical Walkthrough: From Issue to Pull Request

One of the most powerful agentic workflows in Copilot is its ability to take a well-defined GitHub issue and work on it autonomously.

Define the Task: Start with a clear, well-scoped GitHub issue. The more specific the issue description, the better the agent will perform.
Initiate the Agent: Use Copilot Chat to direct the agent. You can assign it a GitHub issue directly. The coding agent will then create a new branch for the task.
Code and Test Iteration: The agent will read the relevant files, write the necessary code to address the issue, and run tests to verify its changes. If tests fail, it will attempt to debug and correct the code.
Monitor and Guide: You can monitor the agent's progress and provide feedback or further instructions through the multi-turn conversation in Copilot Chat.
Review the Pull Request: Once the agent determines the task is complete, it will open a pull request. This "issue-to-PR" workflow allows you to review the changes, and Copilot can even assist in automating code review on the PR.

Leveraging Multi-Agent Orchestration

With the release of VS Code 1.109, Copilot introduced multi-agent orchestration. This advanced feature allows a primary agent to delegate tasks to parallel subagents. For example, when working on a feature that touches separate frontend and backend modules, the system can spin up independent subagents to work on each part simultaneously. This aids in both the planning and review stages, positioning VS Code as a central hub for multi-agent development.

Limitations and Strategic Considerations

While Copilot agents are powerful, it's crucial to understand their limitations and the security implications of their use. This knowledge helps you choose the right tool for the job and operate safely.

When to Use Other Tools: Copilot vs. Alternatives

Copilot performs best on scoped, single-issue tasks. For highly complex problems, other tools might be more suitable. Copilot's multi-file editing is functional but considered weaker than its competitors, and as an extension, it cannot modify the core editor experience as deeply as a native AI-first IDE.

Option	Strengths	Best for
GitHub Copilot	Issue-to-PR automation, native code review, multi-model flexibility, works across many IDEs, strong team workflow integration.	Team-level AI workflows, well-defined GitHub issues, code review automation, and budget-conscious teams.
Cursor	Superior autocomplete and multi-line edits, seamless inline editing (Cmd+K), background parallel agents, Composer-level codebase understanding.	Daily editing tasks, routine code generation, multi-file changes, and developers prioritizing a native AI-first editor experience.
Claude Code	Massive 1M token context window, excels at tasks requiring deep codebase understanding, Agent Teams for collaborative problem-solving.	Large-scale refactors, architecture changes, security audits, and debugging subtle, cross-file issues.

Security Considerations When Using Copilot Agents

Automating tasks with AI agents that can read files and execute commands introduces security considerations. To mitigate risks:

Implement Human Oversight: Never allow agents to push code to a production branch without review. Use approval gates, like required pull request reviews, for any changes, especially nuanced or critical ones.
Separate Secrets Injection: Ensure that the agent's environment does not have direct access to production keys or secrets. Manage credentials securely and outside the agent's direct control.
Validate Context: Be mindful of the files and data the agent has access to. Context pruning and validation are important to prevent the agent from acting on incorrect or sensitive information.
Monitor and Alert: Add alerts for unusual agent activity, such as repeated failures or attempts to access restricted files, to catch potential issues early.

Troubleshooting Common Agent Issues

Agent Struggles with Complex Tasks: If an agent is failing to make progress on a large refactor or architectural change, the task may be too complex for its context awareness. Solution: Break the task into smaller, more manageable sub-tasks. For very large or subtle issues, consider a tool with a larger context window, like Claude Code.
Latency During Operations: Because Copilot is a plugin, its architecture can add latency for each file operation compared to a native AI editor. Solution: If speed is a primary concern for frequent, small edits, a tool like Cursor might feel more responsive.
Hitting Usage Limits: The Copilot Pro plan has a limit of 300 premium requests per month. Heavy users may find their responses falling back to base models. Solution: Monitor your usage. For enterprise-level, high-volume needs, explore GitHub Copilot Enterprise plans.

Future of AI Agents in Copilot

The landscape of AI-powered code editors is rapidly advancing. Upcoming developments for Copilot include:

Background Agents: GitHub Copilot Workspace is testing "issue-to-PR pipelines" that automatically transform a GitHub issue into a proposed code change and pull request, operating in the background.
Enhanced Team Collaboration: AI-driven stacked PR workflows, which have already boosted productivity at companies like Shopify, indicate a future where AI agents significantly improve team velocity and reduce merge times.

Frequently Asked Questions

What are AI agents in GitHub Copilot?

AI agents in GitHub Copilot are autonomous sessions that can handle multi-step tasks, read and modify files, execute commands, and iterate until a task is complete.

How do I interact with Copilot agents?

You primarily interact with Copilot agents through Copilot Chat in VS Code, which allows for multi-turn conversations to assign tasks, ask questions, and guide the agent's work.

What are the security risks of using Copilot agents?

Risks include unintended code changes and potential exposure of secrets if not managed properly. Always use human oversight via pull requests and keep sensitive credentials separate from the agent's environment.

Can Copilot agents perform complex refactoring?

Yes, Copilot's coding agent can handle architecture-level refactoring. However, for tasks requiring extremely deep codebase understanding across many files, a tool with a larger context window like Claude Code may be more effective.

What is the cost of using Copilot agents?

The full agentic capabilities are available in the paid GitHub Copilot Pro plan ($10/month), which includes 300 premium requests per month. A free tier offers limited completions and chat messages.

Can I customize Copilot agent behavior?

Yes, you can add custom instructions to the agent system prompt. These instructions are always the last thing in the prompt, allowing you to influence the agent's behavior and tailor its responses.

Conclusion

Creating and leveraging AI agents in GitHub Copilot empowers developers to automate complex, multi-step tasks, from code generation to complete issue-to-PR workflows. By understanding the core concepts and directing agents through Copilot Chat, you can significantly boost productivity. However, it's equally important to recognize their current limitations, know when to use alternative tools, and implement security best practices like human oversight and careful credential management. As Copilot continues to evolve with features like background agents and enhanced team collaboration, its role as a central connective tissue for AI-assisted development is set to expand, making agentic workflows a standard part of the modern developer's toolkit.

Sources & References

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