Beginner

Introduction to GitHub AI

A complete map of every AI capability GitHub offers in 2025-2026 - and how they fit together to transform how you write, review, and ship code.

GitHub's AI Transformation

GitHub has evolved from a code hosting platform into the world's most AI-integrated development environment. Since the launch of GitHub Copilot in 2022, the platform has rapidly expanded its AI capabilities across every stage of the software development lifecycle - from planning and writing code to reviewing pull requests, running CI/CD pipelines, and scanning for security vulnerabilities.

As of 2025-2026, GitHub's AI features are no longer isolated tools. They form an interconnected ecosystem where AI assists you at every step. Understanding this ecosystem is the first step to leveraging it effectively in your daily work.

The GitHub Copilot Ecosystem

GitHub Copilot is not a single product - it is a family of AI-powered capabilities that share a common foundation. Here is how the pieces fit together:

• Copilot Code Completion - Real-time inline suggestions as you type in VS Code, JetBrains, Neovim, and other editors. This is the original Copilot experience: ghost text that you accept with Tab.
• Copilot Chat - A conversational AI assistant available inside your editor, on GitHub.com, and in the GitHub Mobile app. Ask it to explain code, generate tests, debug errors, or refactor functions.
• Copilot Coding Agent - An autonomous agent that can be assigned a GitHub Issue. It reads the issue, plans the changes, writes code, runs tests, and opens a pull request - all without human intervention.
• Copilot Workspace - An AI-powered development environment on GitHub.com where you brainstorm, plan, and implement changes collaboratively with AI before pushing to a branch.
• Copilot in the CLI - AI assistance directly in your terminal for composing shell commands, explaining errors, and generating scripts.
• Copilot Extensions - Third-party integrations that extend Copilot Chat with domain-specific knowledge from tools like Docker, Sentry, LaunchDarkly, and more.

Beyond Copilot: GitHub Models & AI Infrastructure

GitHub is not just consuming AI - it is providing AI infrastructure to developers. GitHub Models is a marketplace built directly into GitHub.com where you can discover, test, and deploy large language models from providers like OpenAI, Anthropic, Meta, Mistral, and Cohere.

With GitHub Models you can:

• Try models in an interactive playground without writing any code
• Compare outputs from different models side by side
• Get code snippets to integrate any model into your application using the Azure AI Inference SDK
• Deploy models to Azure with a single click for production workloads

This means GitHub has become a one-stop shop: you discover models, prototype with them, build your app with Copilot's help, and deploy - all without leaving the GitHub ecosystem.

AI Across the Development Lifecycle

GitHub's AI features now touch every phase of software development. Here is where AI shows up:

Phase	AI Feature	What It Does
Planning	Copilot in Issues	Summarizes issue discussions, suggests labels, and helps draft issue descriptions
Coding	Copilot Code Completion	Real-time inline code suggestions across all major editors
Coding	Copilot Chat	Conversational AI for explaining, generating, and refactoring code
Coding	Copilot Coding Agent	Autonomously implements features from GitHub Issues
Coding	Copilot Workspace	AI-powered brainstorming and implementation environment
Terminal	Copilot CLI	AI-generated shell commands and explanations
Code Review	Copilot in Pull Requests	AI-generated PR summaries, review comments, and suggested fixes
CI/CD	AI in GitHub Actions	AI-assisted workflow generation, failure analysis, and debugging
Security	AI-Powered Security	Copilot Autofix for code scanning alerts, secret scanning, and vulnerability remediation
Deployment	GitHub Models	Discover, test, and deploy LLMs directly from GitHub

Comparing GitHub AI Feature Tiers

Not all AI features are available on every plan. Here is a quick comparison:

Feature	Free	Pro ($10/mo)	Business ($19/user)	Enterprise ($39/user)
Code completions	2,000/month	Unlimited	Unlimited	Unlimited
Copilot Chat messages	50/month	Unlimited	Unlimited	Unlimited
Coding Agent	Limited	Yes	Yes	Yes
Model selection	Limited	Full	Full	Full + fine-tuning
PR summaries	No	Yes	Yes	Yes
Copilot Workspace	No	Yes	Yes	Yes
Admin controls & policies	No	No	Yes	Yes
Audit logs & IP indemnity	No	No	No	Yes

Where AI Agents Fit in Your Workflow

The term "AI agent" refers to an AI system that can act autonomously - not just answer questions, but take actions, make decisions, and produce tangible outputs. On GitHub, the most important agent is the Copilot Coding Agent.

Here is a typical workflow using AI agents:

1. Create an Issue - Describe the feature, bug fix, or refactoring task in a GitHub Issue with clear acceptance criteria.
2. Assign to Copilot - Assign the issue to Copilot (or tag @copilot). The agent reads the issue and begins planning.
3. Agent Works Autonomously - Copilot creates a branch, writes code, generates or updates tests, and runs CI checks in a secure cloud environment.
4. Review the PR - The agent opens a pull request with a summary of changes. You review it just like any human-authored PR.
5. Iterate or Merge - Leave review comments and the agent will address them, or approve and merge when satisfied.

This workflow does not replace developers - it amplifies them. You focus on architecture decisions, code review, and the creative aspects of software engineering while the agent handles well-defined implementation tasks.

What You Will Learn in This Course

This 12-lesson course takes you through every major AI feature on GitHub, with hands-on examples and practical guidance:

• Lessons 2-3: Master Copilot code completion and chat in your editor
• Lesson 4: Use the Copilot Coding Agent to implement features autonomously
• Lesson 5: Leverage AI in pull request reviews
• Lesson 6: Explore and deploy models from GitHub Models
• Lessons 7-8: Work with Copilot Workspace and the CLI
• Lessons 9-10: Integrate AI into GitHub Actions and build Copilot Extensions
• Lesson 11: Use AI-powered security scanning and autofix
• Lesson 12: Best practices for AI-assisted development at scale