GitHub Copilot

GitHub Copilot is an AI pair programmer built into your code editor, command line, and GitHub itself. It reads the code you are writing and suggests completions, whole functions, and fixes in real time. Beyond autocomplete, Copilot runs in agent mode, where it plans a task, edits multiple files, runs tests, and reports back, and it ships cloud agents that pick up a GitHub issue and open a finished pull request on their own. It matters for developers building production systems because it collapses the gap between describing a change and shipping it, whether that change is one function or a multi-file refactor across a large codebase.

How GitHub Copilot works

Copilot connects a large language model, an AI system trained to predict text and code from massive datasets, to the context of whatever you are working on: your open files, your repository, and your prompt. It turns that context into suggestions you can accept, edit, or reject.

Here is what happens under the hood.

Code completion: As you type, Copilot suggests the next line or block of code based on the surrounding file and your coding patterns.
Chat: You ask Copilot questions in plain language inside your editor, and it answers using the context of your open files and repository.
Agent mode: You describe a task, and Copilot plans the steps, edits files across your workspace, runs tests, and shows you a diff to approve or undo.
Cloud agents: You assign a GitHub issue to Copilot, and it works in the background, exploring the codebase and opening a pull request when done.
Model Context Protocol (MCP): Copilot connects to external tools and data sources through MCP, an open standard that lets AI agents call APIs, databases, and other services directly.

What you can build with GitHub Copilot

Automated test suites: Prompt Copilot to generate full test coverage for a service, including edge cases like null inputs and timeouts, cutting the time it takes to reach meaningful coverage.
Framework migrations: Point agent mode at a codebase running an old framework version and have it scan for deprecated APIs, then rewrite the affected files in one pass.
Self-serve pull requests: Assign a bug report or feature request as a GitHub issue to a Copilot cloud agent and get a reviewable pull request back without touching the code yourself.
Infrastructure as code: Describe the deployment you need, such as a load-balanced Kubernetes manifest, and let Copilot generate the configuration files.
Onboarding to a new stack: Use Copilot Chat to ask questions about an unfamiliar codebase or framework and get answers grounded in the actual repository instead of generic documentation.
Custom internal tooling: Connect Copilot to your company’s APIs and internal tools through MCP so it can pull real data into its suggestions and agent runs.