GitHub Copilot Modes Compared | Generated by AI
Overview
GitHub Copilot offers several modes for code assistance in IDEs like VS Code and Visual Studio, including Edit Mode (which aligns with your description of “fix mode” for selecting and repairing specific lines or functions) and Agent Mode (which handles broader, autonomous edits across entire files or projects). Edit Mode is precise and user-controlled, ideal for targeted fixes, while Agent Mode acts like an AI pair programmer, reasoning through complex tasks and making multi-file changes without needing constant input.[1][1] Both modes enhance productivity but differ in scope, autonomy, and workflow.
Key Differences
Edit Mode focuses on user-selected code snippets, providing suggestions for review and approval before applying changes. In contrast, Agent Mode operates on a higher level, analyzing the full codebase context to plan, execute, and iterate on edits autonomously, often modifying whole files or related components to maintain consistency.[2][1] Here’s a side-by-side comparison:
| Feature | Edit Mode (Fix Mode) | Agent Mode |
|---|---|---|
| Scope | Limited to selected lines, functions, or a single file. You highlight code to fix bugs, refactor, or improve specific parts.[1] | Entire workspace or project. It identifies and edits related files automatically, beyond what you select.[2][3] |
| User Control | High: Suggests changes for your review and explicit approval. You define exactly what to edit.[4] | Medium: Applies edits automatically but flags risky commands (e.g., terminal runs) for review. You set the goal via natural-language prompts.[1][1] |
| Autonomy | Low: Provides targeted suggestions; doesn’t reason across files or run independent actions.[1] | High: Reasons step-by-step, runs tests/commands, detects errors, and self-corrects. Maintains context over sessions.[2][3] |
| Response Time | Fast: Quick analysis of selection only.[2] | Slower: Analyzes full project context, which may take longer for large codebases.[2] |
| Best For | Quick fixes like debugging a function, optimizing a loop, or rewriting a method without broader impact.[1] | Complex tasks like refactoring across files, generating tests for a module, migrating code, or building features from scratch.[3][5] |
| Examples | - Select a buggy function: “Fix this null check.” - Highlight lines: “Make this async.” [2] |
- Prompt: “Refactor the entire service layer to use async/await and update all dependencies.” - Or: “Modernize this Java project to JDK 21 across files.” [5][6] |
| Risks/Limitations | Minimal risk, as changes are isolated; but requires manual selection for each fix.[1] | Higher autonomy can lead to unintended changes; always review diffs. Not ideal for highly controlled environments.[7][4] |
Use Cases and Workflows
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Edit Mode for Targeted Fixes: Use this when you know exactly what’s wrong, e.g., selecting error-prone code in a function to resolve a bug or improve performance. It’s like a “spot edit” tool—select the code in your IDE, prompt Copilot via chat (e.g., “@workspace /fix”), and apply the diff preview. This mode shines in iterative development where you want to maintain full control and avoid overhauling unaffected areas. For instance, in a .NET project, you might select a method and ask, “Identify null reference exceptions and suggest fixes” for that snippet only.[2][8] It’s available in VS Code and Visual Studio with GitHub Copilot extensions.
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Agent Mode for Project-Wide Edits: Activate this for holistic changes, such as when you need to edit entire files or coordinate updates across a codebase. Start a session in Copilot Chat (e.g., “#agentmode” or via the dropdown), give a high-level prompt like “Find all uses of deprecated API and migrate to the new one in this project,” and watch it plan steps: analyzing files, proposing edits, running tests, and iterating. It can create new files, update namespaces, or even scaffold sections of an app. In Java modernization, for example, it scans a legacy project, updates Gradle dependencies, and validates changes across multiple files.[5][3] This mode is particularly powerful for refactoring, bug hunting at scale, or automating repetitive tasks like adding documentation or tests.[6][9]
Agent Mode builds on Edit Mode’s foundation but expands it—think of Edit as a scalpel for precise cuts, and Agent as a surgeon handling the full operation.[1] Custom instructions (e.g., via VS Code settings) can guide Agent Mode for consistency, like enforcing naming conventions across edits.[1]
When to Choose Each
- Opt for Edit/Fix Mode if your task is localized (e.g., fixing a single function) to keep things simple and fast.
- Switch to Agent Mode for efficiency on larger scopes, like whole-file edits or multi-step refactors, but start small to build trust in its outputs.[2][1] Both require a GitHub Copilot subscription (Pro or higher) and are integrated into VS Code/Visual Studio. For best results, combine them: Use Edit for initial fixes, then Agent for propagating changes project-wide.[8] If you’re dealing with sensitive code, review all Agent proposals carefully to avoid surprises.[7]
Citations: [1] https://github.blog/ai-and-ml/github-copilot/copilot-ask-edit-and-agent-modes-what-they-do-and-when-to-use-them/ [2] https://devblogs.microsoft.com/dotnet/ask-mode-vs-agent-mode/ [3] https://github.blog/ai-and-ml/github-copilot/agent-mode-101-all-about-github-copilots-powerful-mode/ [4] https://github.blog/developer-skills/github/less-todo-more-done-the-difference-between-coding-agent-and-agent-mode-in-github-copilot/ [5] https://github.blog/ai-and-ml/github-copilot/a-step-by-step-guide-to-modernizing-java-projects-with-github-copilot-agent-mode/ [6] https://code.visualstudio.com/docs/copilot/chat/chat-agent-mode [7] https://www.reddit.com/r/GithubCopilot/comments/1je448f/after_playing_with_agent_mode_i_think_my_favorite/ [8] https://visualstudiomagazine.com/articles/2025/06/18/copilot-compared-advanced-ai-features-in-visual-studio-2022-vs-vs-code.aspx [9] https://github.blog/ai-and-ml/github-copilot/github-copilot-coding-agent-101-getting-started-with-agentic-workflows-on-github/