Agentic AI Development with GitHub Copilot

Discover how GitHub Copilot has evolved from a code completion tool to a powerful platform for building and orchestrating autonomous AI agents that can plan, execute, and learn from your development workflow.

9 min read

The landscape of software development is undergoing a fundamental transformation. GitHub Copilot, which began as an AI-powered code completion tool, has evolved into a sophisticated platform for building agentic AI systems. This article explores what agentic AI development means and how GitHub Copilot is leading this revolution.

What is Agentic AI?

Agentic AI refers to artificial intelligence systems that can act autonomously to achieve goals. Unlike traditional AI tools that simply respond to prompts, agentic AI can:

  • Plan complex multi-step workflows
  • Execute tasks across different tools and environments
  • Learn from past interactions and improve over time
  • Make decisions based on context and goals
  • Adapt to changing requirements and feedback

Think of agentic AI as having a digital assistant that not only understands your commands but can independently figure out how to accomplish tasks while learning from experience.

GitHub Copilot's Evolution

GitHub Copilot started as a code completion tool in 2021, but recent developments have transformed it into a comprehensive agentic platform. Here's how it evolved:

From Assistant to Agent

Traditional Copilot (2021-2023):

  • Autocomplete code suggestions
  • Generate code from comments
  • Respond to inline prompts

Agentic Copilot (2024-Present):

  • Autonomous task completion
  • Multi-step planning and execution
  • Cross-tool orchestration
  • Memory and learning capabilities
  • Custom agent creation

Key Agentic Features in GitHub Copilot

1. GitHub Copilot Coding Agent

The coding agent represents GitHub Copilot's most autonomous form. It can:

  • Understand complex requests and break them into actionable steps
  • Edit multiple files across your codebase simultaneously
  • Run tests and fix failures automatically
  • Review its own work and iterate until completion

Example use case: Instead of writing "create a login page," you can ask the coding agent to "implement authentication with JWT, including login/signup pages, password reset, and email verification." The agent will plan the entire implementation, create necessary files, write tests, and ensure everything works together.

2. GitHub Copilot CLI

The Copilot CLI brings agentic capabilities to your terminal, allowing you to:

  • Execute complex bash commands using natural language
  • Chain multiple operations automatically
  • Troubleshoot errors with context-aware suggestions
  • Automate repetitive terminal workflows

Practical example:

Terminal
gh copilot suggest "find all TypeScript files modified in the last week and run tests on them"

The CLI will generate the appropriate command pipeline and execute it.

3. GitHub Copilot SDK

Released in technical preview, the GitHub Copilot SDK enables developers to build custom agents. The SDK provides:

  • Tool invocation capabilities to interact with external services
  • File editing operations within codebases
  • Command execution across different environments
  • Programmable agent behavior for specific use cases

This means you can create specialized agents for your organization's unique workflows—for example, an agent that automatically generates documentation, or one that enforces coding standards across pull requests.

4. Agentic Memory System

One of the most powerful features is Copilot's memory system:

  • Learns from your codebase patterns and conventions
  • Remembers previous interactions within a project
  • Shares knowledge across agents (coding agent, CLI, code review)
  • Improves suggestions based on accumulated context

This memory persists across sessions, meaning Copilot gets smarter about your specific project over time.

5. Mission Control

Mission control provides a centralized interface to:

  • Orchestrate multiple agents working on different tasks
  • Monitor agent progress in real-time
  • Review agent outputs before applying changes
  • Manage agent workflows efficiently

This is particularly useful when you have several agents working on different aspects of a large feature.

Building Your First Agentic Workflow

Here's a practical guide to getting started with agentic AI development using GitHub Copilot:

Step 1: Set Up Your Environment

  1. Install GitHub Copilot in your IDE (VS Code, Visual Studio, or JetBrains)
  2. Enable Copilot Chat for conversational interactions
  3. Install GitHub CLI with Copilot extension: gh extension install github/gh-copilot

Step 2: Create an agents.md File

This file instructs agents on how to work with your codebase:

Step 3: Use the WRAP Framework

When working with Copilot coding agent, use the WRAP framework for effective prompts:

  • Write clear, specific issues
  • Refine instructions based on results
  • Add context about your project
  • Provide examples when needed

Bad prompt: "Fix the bug"

Good WRAP prompt: "The user registration form in src/components/Auth/Register.tsx doesn't validate email formats. Add email validation using regex that checks for standard email format (username@domain.com), display an error message below the input field, and add a test case in Register.test.tsx to verify the validation works."

Step 4: Leverage Agent Orchestration

For complex features, break work into agent-manageable tasks:

Task 1: Create database schema for user profiles
Task 2: Build API endpoints for profile CRUD operations
Task 3: Design frontend profile edit component
Task 4: Write integration tests
Task 5: Update documentation

Assign each task to Copilot coding agent and use mission control to track progress.

Best Practices for Agentic Development

1. Provide Rich Context

Agents work best with clear context:

  • Use descriptive file names and folder structures
  • Write comments explaining complex logic
  • Maintain up-to-date README files
  • Create comprehensive agents.md files

2. Review Agent Output

While agents are powerful, always review their work:

  • Check generated code for logic errors
  • Verify security implications
  • Ensure adherence to project standards
  • Test thoroughly before merging

3. Iterate and Refine

Agentic workflows improve through iteration:

  • Start with clear, specific requests
  • Review the agent's approach
  • Provide feedback and refinements
  • Let the agent learn from corrections

4. Use Slash Commands

GitHub Copilot CLI offers slash commands for common tasks:

  • /test - Run tests and fix failures
  • /fix - Identify and fix code issues
  • /explain - Explain complex code
  • /doc - Generate documentation

5. Secure Your Agents

Follow agentic security principles:

  • Least privilege: Agents should have minimal necessary permissions
  • Validation: Verify all agent inputs and outputs
  • Auditability: Log agent actions for review
  • Sandboxing: Run agents in isolated environments when possible

Real-World Use Cases

Use Case 1: Automated Code Reviews

Create an agent that:

  • Reviews pull requests for common issues
  • Checks coding standards compliance
  • Suggests performance improvements
  • Identifies security vulnerabilities

Use Case 2: Documentation Generation

Build an agent to:

  • Generate API documentation from code
  • Create user guides from feature implementations
  • Update README files automatically
  • Maintain changelog entries

Use Case 3: Test Automation

Develop an agent that:

  • Writes unit tests for new functions
  • Generates integration test scenarios
  • Creates end-to-end test suites
  • Maintains test coverage reports

Use Case 4: Dependency Management

Configure an agent to:

  • Monitor for dependency updates
  • Test compatibility with new versions
  • Generate upgrade PRs automatically
  • Document breaking changes

Custom Agents with Partner Integrations

GitHub Copilot now supports partner-built custom agents for specialized tasks:

  • Observability agents (Datadog, Elastic): Debug performance issues
  • Infrastructure agents (HashiCorp, MongoDB): Manage cloud resources
  • Security agents (Snyk, StackHawk): Identify vulnerabilities
  • Testing agents (Diffblue): Generate comprehensive test suites

These agents integrate directly into your workflow, accessible via the terminal, editor, or GitHub.com.

Measuring Agent Effectiveness

Track these metrics to assess your agentic workflows:

  1. Task completion rate: Percentage of tasks completed without intervention
  2. Time saved: Hours recovered by automating repetitive work
  3. Code quality: Defect rates in agent-generated code
  4. Developer satisfaction: Team feedback on agent usefulness
  5. Iteration count: How many refinements agents need

The Future of Agentic Development

The agentic AI landscape is rapidly evolving. Here's what's on the horizon:

Emerging Trends

  • Multi-agent collaboration: Different specialized agents working together
  • Proactive agents: Agents that identify and fix issues before they're reported
  • Cross-repository learning: Agents that learn from multiple projects simultaneously
  • Natural language programming: Writing complex applications through conversation

Preparing for the Future

To stay ahead:

  • Experiment with agentic workflows today
  • Build expertise in prompt engineering
  • Understand agent capabilities and limitations
  • Contribute feedback to improve agent systems

Getting Started Today

Ready to begin with agentic AI development? Here's your action plan:

  1. Week 1: Install GitHub Copilot and explore chat features
  2. Week 2: Try GitHub Copilot CLI for terminal automation
  3. Week 3: Use coding agent for a small feature
  4. Week 4: Create your first custom agent with the SDK
  5. Ongoing: Refine your agents.md and iterate on workflows

Conclusion

Agentic AI development with GitHub Copilot represents a paradigm shift in how we build software. Instead of writing every line of code, developers become orchestrators—defining goals, providing context, and guiding autonomous agents to implement solutions.

The key to success is understanding that agents are tools to amplify human creativity and decision-making, not replace them. Start small, experiment often, and gradually integrate agentic workflows into your development process.

As these systems continue to evolve, the developers who master agentic AI will be the ones who can ship faster, build better, and innovate more effectively.

Resources

This article was written to help developers understand and leverage the agentic capabilities of GitHub Copilot. As the technology evolves, we'll continue to update this guide with new insights and best practices.