Complete Guide to GitHub Copilot Agent Mode: Transforming Development Paradigms

New Development Experiences Enabled by AI Development Agents

The Long-Awaited Addition of Agent Mode to GitHub Copilot

The long-awaited GitHub Copilot Agent Mode has finally begun its general release. I've been committed to using Roo Code rather than Cursor because it was a Visual Studio Code (VSCode) extension. Since VSCode is developed by Microsoft, although new contenders might temporarily surpass it in speed, I believe that scale economics favors established developers in the long run. That's why GitHub Copilot's Agent Mode is the most promising option for me.

However, to state my conclusion upfront, it still doesn't quite match tools like Roo Code in its current state. Roo Code has implemented a boomerang mode (from a previous article: AI Orchestration Innovation: Development Automation through Boomerang Mode) that allows AI to implement more autonomously. Therefore, I expect further improvements in GitHub Copilot's Agent Mode. That said, while tool selection can be flexible for individual development, organizational development comes with more constraints, so GitHub's improvements are encouraging.

Agent mode coding has already become commonplace with Cursor, Roo Code, WindSurf, and others, but many people may not have tried it yet. In this article, I'll start by explaining how development works in agent mode, and also introduce GitHub's future developments. In particular, Project Padawan, which currently only has limited information available, is a feature I'm particularly looking forward to.

The Beginning of a New Approach to Development

This is not merely a feature update, but something that could fundamentally change the way we develop. This article comprehensively explains the details of this new feature introduced to VS Code, how to set it up, how it differs from alternatives, and its future prospects. We'll explore the new era of development techniques and their significance through AI-human partnerships.

Looking back at the history of software development, tools and methods for improving productivity have continually evolved. From compilers and linters to integrated development environments (IDEs), and more recently to AI-powered code completion, there has been a consistent trend toward automating some of the intellectual work of developers. However, GitHub Copilot's Agent Mode has the potential to bring about a paradigm shift distinct from previous evolutions.

The Role of AI in Current Development Environments

Until now, AI coding assistance has primarily been "suggestive." Since GitHub's Copilot appeared in 2021, code auto-completion has certainly improved many developers' productivity. However, these tools were limited to "suggesting candidates" or "partial automation." As developers organize their thoughts and begin writing code, AI predicts and suggests the continuation—in this relationship, AI has been merely an "assistant," with humans always maintaining control.

The Innovation of Copilot Agent Mode

The innovation of Agent Mode lies in expanding AI's role from a mere "suggester" to an "autonomous executor." This feature, which was previewed in VS Code Insiders on February 24, 2025, and became available in the Stable version in early April 2025, has the potential to fundamentally redefine the relationship between developers and AI¹.

In Agent Mode, developers simply communicate their intentions in natural language, and AI autonomously executes a series of coding tasks. Beyond mere code generation, it supports the entire development process, including editing multiple files, executing necessary terminal commands, and detecting and fixing errors.

This difference in workflow is fundamental. Traditional AI coding assistance followed a "reactive" model where developers would write code fragments and AI would complete them. In contrast, Agent Mode has evolved into a "proactive" model where developers set goals, and AI independently devises and executes the steps to achieve them.

Moving Beyond "Vibe Coding"

In early 2025, Andrej Karpathy introduced the concept of "Vibe Coding" - a method of coding by feel, where you rely entirely on LLMs without understanding code details, and when errors occur, you copy the error message and ask the AI to fix it². While this approach is effective for prototyping, it has been considered unsuitable for production-quality code development.

Copilot Agent Mode has the potential to leverage the advantages of Vibe Coding (improved development speed) while overcoming its drawbacks (uncertainty in code quality). Although the agent operates autonomously, its process is highly transparent, and developers can intervene at each step or revert changes. In other words, it offers a new collaborative model of "managed autonomy" under human supervision.

This new paradigm has the potential to transform the developer's role from a "coder" who writes code to a "director" who supervises AI's work and ensures quality. It might free us from immersion in technical details and allow us to focus on broader design and creative problem-solving.

Copilot Agent Mode Features and How to Use Them

To effectively utilize Agent Mode, you need to accurately understand its features and characteristics. Here I'll explain its core functions in detail and how they transform the development process.

Autonomous Code Generation and Modification

The most basic function of Copilot Agent Mode is the ability to generate code from natural language instructions and modify it as needed. While this was possible with conventional Copilot Chat, the process is now more autonomous and seamless in Agent Mode.

A notable point is that code changes proposed by the agent are immediately applied to files³. Whereas in Chat Mode, they remained "suggestions" that developers needed to explicitly apply, in Agent Mode changes are automatically reflected. You can use the "Undo Last Edit" function to revert changes if necessary, ensuring that AI judgments are always under human control.

// Typical usage example of Agent Mode
// Instruction: "Implement user authentication"
 
// Code automatically generated by the agent
import { useState } from "react";
import { signIn, signOut, useSession } from "next-auth/react";
 
export function AuthButton() {
  const { data: session } = useSession();
  const [loading, setLoading] = useState(false);
 
  const handleAuth = async () => {
    setLoading(true);
    if (session) {
 {
      await signOut();
    } else {
      await signIn();
    }
    setLoading(false);
  };
 
  return (
    <button
      onClick={handleAuth}
      className="px-4 py-2 bg-blue-600 text-white rounded"
      disabled={loading}
    >
      {loading ? "Processing..." : session ? "Sign Out" : "Sign In"}
    </button>
  );
}

Automating Multi-Step Tasks

The true value of Agent Mode lies in its ability to automatically execute tasks spanning multiple steps from a single instruction⁴. For example, in response to the instruction "Build a simple API server with authentication using Express," it will automatically execute steps like these:

1. Planning the necessary file structure
2. Creating or updating package.json
3. Suggesting commands to install required libraries
4. Implementing server code, routing, and authentication logic
5. Creating environment configuration files
6. Adding simple test code

Being able to execute such a series of tasks from a single prompt greatly increases efficiency, especially in the initial stages of development or for routine tasks. It's particularly powerful for generating boilerplate for new projects or adding features to existing projects.

Self-Healing Capability

One of the most innovative aspects of Agent Mode is its ability to detect and self-repair errors³. With traditional AI coding assistance, when generated code contained errors, developers needed to manually detect them and ask the AI to fix them.

In Agent Mode, it automatically analyzes compilation and test execution results, and when errors are found, it identifies their causes and attempts fixes. This process repeats as necessary until the task is completed.

In-house benchmark evaluations (SWE-bench) report that Copilot Agent using Claude 3.7 was able to automatically complete 56% of tasks³. This demonstrates an evolution from simple code completion to automation of more complex development tasks.

Usage Examples and Practical Scenarios

While there are many ways to use Agent Mode, here are some particularly effective scenarios:

Launching New Projects: With instructions like "Create the basic structure for a Todo app with React + TypeScript + Tailwind CSS," you can automate everything from project structure design to basic component implementation.

Extending Existing Codebases: Instructions like "Add dark mode support to this React component" can automatically implement theme switching functionality and style adjustments.

Refactoring and Optimization: Instructions like "Convert this JavaScript code to TypeScript to improve type safety" can automatically perform code conversion and add type definitions.

Documentation Generation: Instructions like "Create a README.md file for this project, including installation instructions and API documentation" can automate documentation creation.

These examples show that Agent Mode is not just a coding assistance tool but a versatile assistant capable of automating many aspects of the development workflow.

Features and Return on Investment for Each Plan

Copilot offers various plans, each with different features and limitations. Here I'll compare the features of each plan and consider the optimal choice from an ROI perspective.

Comparison of Free, Pro, and Pro+ Plans

With the introduction of the Copilot Free plan in late 2024, individual developers can now experience Copilot for free⁴. However, there are clear limits: 2,000 code completions and 50 chat inquiries per month.

On the other hand, paid plans include Copilot Pro ($10/month) and **Copilot Pro+** ($39/month), which was newly established in April 2025³. These plans have no limits on completions or chats and provide access to more advanced models.

For businesses, there's Copilot Business ($19 per user per month) and **Copilot Enterprise** ($39 per user per month)⁵. These include additional organization-oriented features like management functions and policy controls.

Comparing these plans, Copilot Pro appears to be the most cost-effective option for individual developers. At $10 per month for unlimited use, it offers sufficient value for developers who code for several hours each day.

Meanwhile, Pro+ is suitable for power users who want to actively use the latest AI models, or developers who plan to frequently use Agent Mode. With a quota of 1,500 premium requests per month, it's powerful for those who heavily use complex tasks with high-performance models.

Available AI Models and Their Performance Differences

Copilot utilizes multiple AI models, with different models available depending on your plan⁶.

Base Models (unlimited for all plans):

• OpenAI GPT-4o (optimized GPT-4)
• Anthropic Claude 3.5 Sonnet

Premium Models (paid plans only, consumes request quota):

• OpenAI GPT-4.5 (next-generation model)
• OpenAI o3-mini (high-efficiency model)
• Anthropic Claude 3.7 Sonnet/Thinking (advanced reasoning model)
• Google Gemini 2.0 Flash (fast response model)

The performance difference between models becomes apparent with the complexity of development tasks. While base models are sufficient for simple code completion and question answering, premium models tend to have higher success rates for complex refactoring, multi-file editing, and tasks requiring deep reasoning.

Selecting the Optimal Plan for Organizational Use

When considering Copilot adoption for organizations, you should consider Business/Enterprise plans rather than simply providing individual plans to each developer. There are several reasons for this.

Considerations for medium-sized teams (5-50 people):

• With Copilot Business, organization administrators can analyze user activity
• Security and compliance policy settings are available
• Consistency across the organization can be ensured, such as restricting the use of specific models

Considerations for large organizations (50+ people):

• Copilot Enterprise offers large organization-oriented features like Enterprise Managed Users support⁷
• Easier deployment of standardized usage methods, such as sharing custom instructions across the organization
• Improved search performance through semantic indexing of internal repositories

When introducing Copilot to an organization, it's important to consider not just cost comparisons but also ease of management and security aspects. Business or Enterprise plans are recommended especially for organizations handling highly confidential code, as they allow appropriate policy settings and security management.

Cost-Effectiveness Analysis

To evaluate the cost-effectiveness of development support tools like Copilot, you need to compare the value brought by increased developer productivity against the cost of the tool.

According to GitHub's official data, 88% of developers using Copilot report improved productivity, with time savings of about 30% for coding tasks⁸. Agent Mode has the potential to push this even higher.

If we assume a developer's monthly salary is $10,000 and productivity improves by 20$2,000 per month. Compared to this, Copilot Pro's monthly fee of $10 represents only 0.5% of the cost.

Additional indirect effects should also be considered:

• Improved developer satisfaction through freedom from monotonous tasks
• Promotion of innovation by concentrating on more creative problem-solving
• Quick adaptation to new technologies and improved code quality

From this comprehensive perspective, Copilot can be considered a tool with extremely high ROI (Return on Investment), especially in regions with high labor costs, if utilized appropriately.

Comparison with Other AI Coding Tools

Several autonomous coding support tools similar to Copilot Agent Mode have emerged. Here I'll look at representative tools like Cursor, WindSurf, and Roo Code.

Feature Comparison with Cursor, WindSurf, and Roo Code

Cursor is a dedicated IDE forked from VS Code that supports AI-powered code generation and editing. Its standout feature is the "Composer" mode for active code editing, which can propose and apply changes to multiple files based on natural language instructions⁹. Enabling "Yolo" mode also allows automatic execution of terminal commands. The monthly fee is $20 ($16/month with annual payment).

WindSurf is a VS Code fork developed by Codeium, characterized by code auto-application in "Write" mode. It's optimized for Claude models and offers high response speed⁹. At $15 per month, it also offers a free plan.

Roo Code (formerly Cline) is an open-source VS Code extension that functions as an autonomous agent performing file operations within the editor. It can utilize unique models like DeepSeek and is available for free as a self-hosted solution¹⁰.

When selecting a tool, consider the balance between your organization's code quality requirements and development speed. Organizations with thorough code review processes might be comfortable adopting more aggressive automation tools. Conversely, smaller teams with limited review processes might prefer a more cautious approach.

The ease of integration with existing development environments is also an important factor. Copilot Agent Mode is provided as an official VS Code extension, so it has high compatibility with standard VS Code environments. In contrast, Cursor and WindSurf are provided as standalone IDEs, which might require some adjustment to integrate with existing workflows.

However, I feel there aren't major functional differences between these tools at present. Fork-based tools don't have issues with VS Code extension compatibility, and at this point, I think decisions can be based on price or aligning with tools that colleagues are using. As I mentioned at the beginning, I've been committed to using pure VS Code, so I've mainly been using Roo Code. Going forward, I'll switch between Roo Code for larger tasks and GitHub Copilot Agent for simpler processes.

Setting Up Agent Mode in VS Code

To effectively utilize Copilot Agent Mode, proper setup is necessary. Here I'll explain everything from enabling Agent Mode in VS Code to basic usage methods, as well as how to set up custom instructions and MCP (Model Context Protocol).

Basic Installation and Activation Steps

As of April 2025, Agent Mode is being rolled out gradually, but you can explicitly enable it with these steps¹¹:

1. Update VS Code and code extensions: First, update VS Code to the latest version (1.99 or later) and also update the GitHub Copilot extension

2. Enable in settings: Enable Agent Mode in VS Code settings using either of these methods:

   • Add "chat.agent.enabled": true to the settings JSON
   • Check "Copilot: Enable Agent" in the settings UI

3. Confirm mode: When successfully enabled, a mode switch dropdown will appear in the Copilot chat panel, allowing selection of "Agent" mode

Once Agent Mode is enabled, click the Copilot icon on the left side of VS Code to open the chat panel, and select Agent mode from the dropdown. Then simply enter instructions in natural language, and the agent will autonomously begin working.

Basic Usage of Agent Mode

Using Agent Mode is very simple. Once you've selected the mode, just give instructions in natural language. Examples include:

• "Create a REST API with login functionality using Express"
• "Add i18n support to this React component"
• "Create test code for this TypeScript class"

After submitting an instruction, the agent operates in the following flow:

1. Context understanding: Analyzes project structure and codebase
2. Planning: Determines steps to execute
3. File editing: Creates and edits necessary files
4. Command suggestion: Proposes terminal commands as needed
5. Feedback processing: Attempts fixes if errors occur
6. Completion report: Reports results when the task is complete

Important operations like terminal command execution are always proposed in a way that seeks developer permission. You can approve by clicking the "Run" button or decline with "Cancel." All code changes can be reverted using the "Undo Last Edit" function.

Setting Up Custom Instructions

To further personalize Copilot's responses, you can set up Custom Instructions. This allows you to communicate specific coding styles or priorities to Copilot¹².

Setting up custom instructions at the repository level is most efficient. Simply create a .github/copilot-instructions.md file:

1. Create a .github folder in the root directory of your project (if it doesn't exist)
2. Create a copilot-instructions.md file inside it
3. Write your instructions in markdown format

Here's an excerpt from the .github/copilot-instructions.md file I actually use:

# Memory Bank and Development Rules
 
## Memory Bank Structure
 
Memory Bank consists of required core files and optional context files, with a clear hierarchy.
 
### Core Files (Required)
 
- `projectbrief.md`: The foundational document for the project
- `productContext.md`: Project purpose and problems being solved
- `activeContext.md`: Current work focus and latest changes
- `systemPatterns.md`: System architecture and technical decisions
- `techContext.md`: Technologies used and development environment
- `progress.md`: Progress status and remaining tasks
 
## Development Rules
 
### Core Principles
 
- **Type Safety**: Maximize the benefits of TypeScript and avoid using `any`
- **Performance Optimization**: Properly distinguish between server components and client components
- **Accessibility**: Comply with WCAG 2.1 AA standards
 
### Coding Conventions
 
- **Naming Conventions**:
  - React components: PascalCase (e.g., `ArticleCard.tsx`)
  - Hooks: camelCase with `use` prefix (e.g., `useArticleData.ts`)
  - Utility functions: camelCase (e.g., `formatDate.ts`)
 
... (omitted)

Including such detailed instructions in your repository helps Copilot understand the Memory Bank structure and generate code that adheres to your project's rules. It also allows the entire team to share a consistent development experience.

Personal custom instructions can also be used in conjunction, with personal settings taking precedence. Personal settings can be configured from GitHub.com's Copilot chat under "Personal instructions."

Setting Up and Using MCP (Model Context Protocol)

MCP (Model Context Protocol) is a protocol that extends Copilot agent's functionality, allowing it to access external tools and data sources¹³. For example, it enables direct execution of GitHub repository searches or Issue operations from the agent.

To set up MCP:

1. Prepare an appropriate MCP server: Use the official "GitHub MCP Server" published by GitHub, for example
2. Add a settings file to VS Code: Create a .vscode/mcp.json file directly under the workspace folder and describe the server settings
3. Enable tools: Click the "Tools" button displayed in VS Code's Copilot chat and enable the tools you want to use

// Example of .vscode/mcp.json
{
  "servers": {
    "playwright": {
      "command": "npx",
      "args": ["@playwright/mcp@latest"]
    }
  }
}

Using MCP enables the agent to autonomously execute complex tasks such as "Get the latest Pull Request and update unit tests based on its content." This becomes a powerful tool, especially for large-scale projects and team development.

Future Developments and Possibilities

Copilot and its Agent Mode continue to evolve rapidly. Here, I'll look back at the evolution trajectory so far and consider future developments and their impact on the development paradigm.

GitHub Copilot Feature Evolution Timeline

Copilot has undergone remarkable evolution in a short period. Below are the key milestones and anticipated developments.

Each event is described with accurate dates and content based on official information and reliable sources. Particularly noteworthy is the Project Padawan concept announced in February 2025⁷. It presents a future vision where AI agents completely autonomously complete tasks that developers instruct them to do and create Pull Requests. We're approaching a world where Issue management and AI agents are integrated, and corrective code is proposed without human intervention.

Changes in Development Paradigms Brought by Agent Mode

With the spread of Agent Mode, developers' roles and daily operations could change significantly. The following paradigm shifts are anticipated:

1. Change in Developer Roles: The role shifts from "implementer" who writes code line by line to "architect" or "director" who supervises AI's work and provides direction. In addition to coding skills, the ability to effectively guide and evaluate AI becomes important.

2. Transformation of Development Processes: The traditional flow of "design → implementation → testing → deployment" changes to a cycle of "requirements definition → AI instruction → supervision/evaluation → refinement." The implementation phase in particular is notably shortened, and the overall tempo of software development accelerates.

3. Redefinition of Skill Sets: Rather than detailed knowledge of programming languages or specific technologies, "AI prompt engineering" and "system design capabilities" become more valuable. Especially the ability to give effective instructions to AI becomes a new core skill.

4. Code Quality and Review Processes: Managing the quality of AI-generated code becomes a new challenge. Existing code review processes also change from reviews between humans to "human reviews of AI-generated code."

A particularly notable point is that this paradigm shift has the potential to facilitate bridging between technical and business departments. As code is generated from natural language instructions, people on the business side can become more directly involved in the development process. This could shorten the traditional "business requirements → technical requirements → implementation" translation process, leading to closer integration of business needs and technology.

Rise of New Development Practices

With the spread of Agent Mode, new development practices are expected to emerge.

1. AI-Driven Development: Similar to TDD (Test-Driven Development), a development method that cycles through "requirements → AI prompt → code generation → testing → feedback." An approach of designing test cases first and having AI generate code that meets them could also be effective.

2. Hybrid Team Composition: Team compositions where human developers collaborate with AI agents become commonplace. Role divisions might progress where specific tasks (e.g., boilerplate generation, test code creation) are assigned to AI agents, while humans focus on more creative and strategic tasks.

3. AI-Ready Code: A code design method that considers future AI maintenance. Code structures, naming conventions, and documentation formats that AI can easily understand and modify might become standardized.

4. Standardization of Prompt Engineering: Patterns and best practices for effective AI instructions become systematized and shared/standardized within organizations. Know-how might accumulate in the form of "prompt libraries."

Conclusion: Copilot Agent Mode as a Bridge Between Technology and Business

GitHub Copilot's Agent Mode holds the potential to fundamentally redefine the relationship between humans and AI in software development, beyond merely being an evolution of development support tools.

As we've seen, Agent Mode significantly improves developer productivity through autonomous code generation, multi-step task automation, and self-healing functions. Meanwhile, the developer's role changes from "someone who writes code" to "someone who gives instructions to AI and ensures quality." This change should be viewed not as a threat to developers but as an opportunity to focus on more creative and higher-value tasks.

From the perspective of bridging technology and business, the greatest possibility that Agent Mode brings is the reduction of technical barriers. As code is generated from natural language instructions, people on the business side without technical expertise can become more directly involved in software development. This leads to closer integration of business needs and technology, accelerating the organization's overall digital transformation.

In terms of cost-performance, Copilot Pro, available from $10 per month, can potentially deliver extremely high ROI considering a developer's hourly rate. Particularly in advanced countries with high labor costs, the introduction of AI development support tools is becoming essential to maintaining competitiveness.

Of course, challenges remain. Issues such as quality management and security assurance for AI-generated code, and reconsidering methods for organizational knowledge and know-how inheritance need to be addressed. However, development organizations will be required to optimize partnerships between humans and AI while overcoming these challenges.

Ultimately, technologies like Copilot Agent Mode free developers from monotonous tasks and provide an environment where they can focus on more creative and strategic work. This has the potential to bring about not just productivity improvements but qualitative transformation of software development. Building a new bridge between technology and business to deliver higher-value software to market faster—isn't this the essence of the development paradigm in the age of co-creation with AI?

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