GitHub announced Copilot Workspace in April and has been rolling it out in preview through May and June. It goes considerably further than inline code completion. You describe a task, Copilot creates an implementation plan, edits files across your repo, and lets you review and iterate before anything gets committed.
The workflow difference
Current Copilot completes code as you type and can generate a function from a comment. Copilot Workspace operates at a higher level. You open a GitHub issue, or describe a change in natural language, and Workspace creates a plan: which files need to change, what changes to make, and why. You can review the plan, push back on specific steps, and then let it implement the approved plan across multiple files simultaneously.
For fixing a bug that requires changes in five files across a service, this is qualitatively different from using autocomplete. You are reviewing work rather than writing it, which changes the cognitive load significantly when the bug is understood but tedious to implement.
The context difference
One thing the demos make clear: Copilot Workspace has read the whole repository. It understands the project structure, naming conventions, existing patterns, and how things connect. When it proposes a change, it matches the style and architecture of the surrounding code. That is a step up from completions that only see the current file and recent context.
This is where the model improvements of the last year matter in practice. Larger context windows mean a coding assistant can hold more of your codebase in mind. GPT-4 Turbo and Claude 3 family both expanded context windows significantly. Copilot Workspace uses that capacity.
What this means for code review
If AI tools are routinely producing complete implementations for review rather than line-by-line suggestions, the nature of code review changes. You are evaluating larger units of AI-generated code for correctness, security, and architectural fit. That requires different skills and different tooling than reviewing a pull request written line by line by a human.
Teams that treat AI-generated PRs the same way they treat human-written PRs will miss things. The review checklist needs updating: edge cases the AI could not know about, security implications the model missed, performance characteristics that require domain knowledge to assess. The review bar does not go down. It goes in a different direction.