GitHub launches new AI coding agent that fixes bugs

Copilot helps developers write code—it suggests lines of code or even whole functions while typing. In its latest upgrade, the AI agent will be more active, like a mini-assistant, for the developer. Instead of passively suggesting code, the agent ...

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Developer platform GitHub has unveiled an artificial intelligence (AI) coding agent embedded directly into its AI tool, GitHub Copilot. Users can assign the agent tasks and it can fix bugs and add features on a developer's behalf.

Copilot helps developers write code—it suggests lines of code or even whole functions while typing. In its latest upgrade, the agent will be more active, like a mini-assistant, for the developer. Instead of passively suggesting code, the agent will understand and act on goals.

This agent will be built into Copilot itself, meaning developers don't have to install anything extra, and it will be a part of their workflow.


"Using state-of-the-art models, the agent excels at low-to-medium complexity tasks in well-tested codebases – adding features, fixing bugs, extending tests, refactoring code, improving documentation. It’s all about keeping you in the magical flow state," CEO Thomas Dohmke said in a post on X.

Developers can tell the agent to fix the code, implement a search feature, or carry out autonomous actions like fix bugs by identifying broken logic, or understand context across files.

This makes GitHub Copilot more than just a coding assistant, and more like a coding collaborator. "Built around an integrated, secure and fully customizable development environment powered by GitHub Actions, the Copilot coding agent is amplifying human developers with trust by design," Dohmke added.
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This comes days after Google DeepMind unveiled AlphaEvolve, an AI coding agent backed by its Gemini models. However, AlphaEvolve is designed for performance on algorithmic challenges, and is more research-grade and specialised. GitHub's agent, on the other hand, is designed to work alongside developers in real-world projects.
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