Maximizing Agent Mode in GitHub Copilot

[HarvirChima]

Redefining Developer Productivity

Agent Mode in GitHub Copilot is quietly becoming one of the most powerful tools in a developer’s workflow. It is not just about autocomplete anymore. With Agent Mode, GitHub Copilot becomes a task-oriented collaborator that understands your project, proposes solutions, runs tools, and checks in with you before it changes anything.

The April 2025 release of Visual Studio Code 1.100 brought several upgrades that make using Agent Mode even more seamless and productive. If you haven’t explored it yet, now’s the time.

Agent Mode: Your AI-Powered Coding Assistant

Agent Mode enables GitHub Copilot to:

• Understand your project's structure and context
• Plan and execute tasks based on natural language prompts
• Edit and refactor code, run unit or integration tests, and interact with tools like linters, build systems, and dependency managers to streamline development workflows
• Adjust actions based on outcomes
• Seek your approval before applying changes
• Use project-specific instruction files like copilot-instructions.md to follow your team’s coding conventions, preferred libraries, and style guidelines automatically

Agent mode streamlines your development process, enabling you to focus on complex problems while GitHub Copilot handles routine tasks.

Key Features in VS Code 1.100 that Boost Agent Mode

• Prompt and instruction files now let you guide GitHub Copilot with reusable markdown-based goals and guidelines. You can describe how your team wants code written or which frameworks to prioritize.
• Chat responses are now faster, especially when re-running or continuing requests.
• Multi-window support means you can split your editor and GitHub Copilot chat in a clean, organized way.
• Agent edits are faster and more responsive, even across larger files.
• Streamable HTTP and image support makes it easier for agents to use web APIs or work with server-side tools.
• GitHub repository search directly inside the editor helps you reference real code without switching tabs.

Agent Mode and MCP

One of the more advanced features behind the scenes is Model Context Protocol (MCP). This is what allows GitHub Copilot’s Agent Mode to securely interact with tools and services during a task. MCP acts as a structured connection between the model and your environment, enabling dynamic, context-aware tool usage based on the code you're working on.

With MCP, Agent Mode can run static analyzers, internal APIs, test runners, and custom scripts. It can also integrate with security-focused tools like CodeQL or secret scanning, giving you visibility into vulnerabilities or exposed secrets as part of the development flow. Each tool defines what input it requires and what output it returns, and GitHub Copilot uses that to decide what to do next. Results such as logs, structured responses, or diagrams can be streamed directly into chat, so you can evaluate them in real time without switching context.

All of this happens with structured inputs and secure handling. You can see exactly what was run, why it was used, and what came out of it. It is more than just suggestions; it is task execution with traceability.

MCP also supports custom tooling. You can register your own team’s CLI tools or scripts and let GitHub Copilot run them in response to specific prompts. This opens the door for workflows that are deeply tailored to how your team builds, tests, and ships software.

Prompt Ideas to Try in Agent Mode in GitHub Copilot

• Add input validation to all routes in the user controller using express-validator.
  A simple but essential task where GitHub Copilot can scan the file, add validations, and even test them.

• Find and remove any unused imports and dead code in this file, then run the linter.
  Helpful for cleanup before a pull request. Keeps things tidy without manual review.

• Write integration tests for the login and registration flows using a typical Node.js testing setup.
  Agent Mode can scaffold test files, simulate common user inputs, and verify expected responses.

• Run a security scan with CodeQL and generate a report summarizing critical issues.
  Pulls in GitHub Advanced Security (GHAS) tool integration via MCP, with structured output in the chat.

• Update all API calls in this file to use async/await instead of promise chains.
  A clean refactor task that helps modernize older codebases.

• Review this pull request and summarize any changes related to authentication logic.
  Agent Mode reads the diff and provides a concise summary for reviewers.

• Generate a markdown README with setup instructions, usage examples, and a section for contributing.
  Great for internal tools or side projects that need documentation quickly.

Tips for Getting the Most Out of Agent Mode

• Craft Specific, Outcome-Oriented Prompts:
  When engaging with Agent Mode, frame your requests with clear objectives. For example, instead of saying “Improve this function,” specify “Refactor the processData function to enhance readability and reduce time complexity.” This clarity helps Agent Mode generate more targeted and effective solutions.

• Utilize Prompt and Instruction Files:
  Incorporate .prompt.md and copilot-instructions.md files into your repository. These files guide Agent Mode by setting coding standards, defining project goals, and restricting tool access, ensuring consistent behavior across your team.

• Leverage Contextual Awareness:
  Agent Mode automatically gathers context from your workspace, including the active file, selected text, and repository structure. While keeping relevant files open can enhance context, it's not strictly necessary for Agent Mode to function effectively.

• Iterate with Feedback:
  Engage in a back-and-forth with Agent Mode. If a suggestion isn't perfect, provide feedback or adjust your prompt to refine the output. This iterative process can lead to more accurate and tailored solutions.

• Integrate with Existing Workflows:
  Align Agent Mode's capabilities with your current development processes. Use it to automate repetitive tasks, generate boilerplate code, or assist in code reviews, thereby streamlining your workflow.

• Stay Informed on Updates:
  Regularly check the GitHub changelog and blog for updates to GitHub Copilot. Things are moving fast in this space so new features and improvements can enhance Agent Mode's functionality and your overall development experience.

Parting Thoughts

Agent Mode is already changing how teams think about software tasks. With the new features in VS Code 1.100 and the flexibility provided by MCP, GitHub Copilot becomes a reliable teammate who can do more than just suggest a line of code.

If you are building daily and want to move faster without sacrificing control, this is a good place to start. Try one of the prompts above, and see how far GitHub Copilot can take you.

[dsetzer]

This is not accurate at all

[abo-L]

TON

[abhi-upsteer]

Love the deep dive on Agent Mode—startups not leveraging it today are really missing out, we use it everyday. Super insightful and timely!

[BackwardJet]

I’ve been using GitHub Copilot for a while, but Agent Mode looks like the game-changer my team and I need to take our workflow to the next level. Finally, Copilot's stepping into its (secret) agent era, time to code like 007!

[a-kotte]

I've also been using Copilot for almost a year now, and the agent mode is definitely a step in the right direction. Love how much more productive this tool allows us to be! I especially like the key feature of prompt and instruction files highlighted in this write-up, and definitely plan on using that soon.

[dsetzer]

That feature is not as functional as the write-up makes it sound and even the documentation on this feature is a bit unclear and ambiguous. So to save you the headache:

• First of all the extension is broken and doesn't detect the instruction prompts for some reason when you try to add it using the "add context" button.
• You need to put it not in the repo directory but specifically inside the .github directory within the workspace (if it doesn't exist you have to create it).
• I'm not sure what they have going on with it but it seems copilot is discouraged going near that folder or touching it for any reason except to read this one specific file.
• File naming isn't just "make a instruction file with .prompt.md and copilot automatically considers it", it's specifically copilot-instructions.md even the documentation is incorrect/unclear about this.

Maybe they had plans to make it more inclusive of instruction files but as it is right now it's that specific file, in that specific directory within the workspace and you can't even simply add instructions through the extensions UI because nothing ever shows up in the list of selectable instructions to add to the context..

Could change in the future if they decide to actually implement some of these things they like to talk about but this is current actual reality at the moment.