
What happened
GitHub introduced the Copilot app, a workspace designed around multiple agent sessions connected to projects rather than a single chat window. Developers can switch between tasks, use Quick Chat for side questions, create browser canvases to preview UI changes, and enable Agent Merge to monitor pull requests and assist with review feedback and CI failures.
Why it matters
Real software development involves jumping between bug fixes, code reviews, and exploring unfamiliar codebases — tasks that don't fit a linear chat flow. By anchoring each session to a project and letting developers maintain multiple threads of work simultaneously, the Copilot app aims to reduce time spent preparing environments and context-switching, keeping momentum on different parts of a workflow.
What to watch
Key features include the /create-canvas command to preview applications directly in the app, Pick & Polish to refine UI elements by pointing and requesting changes, and Agent Merge's ability to address review feedback, resolve CI failures, and handle merge conflicts during the pull request process.
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The GitHub Copilot app addresses a fundamental mismatch between how AI chat tools are typically designed and how developers actually work. Traditional chat-based AI coding assistants assume a linear, question-and-answer workflow, but real software development is context-heavy and task-switching is constant. A developer might begin implementing a feature, discover they need to understand an unfamiliar part of the codebase, pause to review a colleague's pull request, and return to the original task—each step requiring different context and tooling.
By anchoring agent sessions to projects and allowing multiple concurrent sessions, the Copilot app preserves context across these transitions. The addition of browser canvas—letting developers visualize UI changes directly within the workflow—bridges the gap between code review and visual feedback, a gap that normally requires opening a separate terminal and browser. Agent Merge's automation of pull request workflows (addressing feedback, handling CI failures, managing merge conflicts) extends the AI's role beyond initial code generation into the full development lifecycle, reducing friction at a stage where many code changes currently stall.
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