Code review is a part of development that's hard to love. Even when everything is written neatly, someone has to go through the changes, find potential issues, and either fix them or leave a comment – and then wait for the author to deal with it. It's slow, requires attention, and even an experienced developer can miss something.
Cursor, the company behind the AI-powered code editor of the same name, has taken a step towards automating this process. They recently introduced Bugbot Autofix – an addition to their existing Bugbot tool, which finds errors in pull requests. The new part is the «autofix», meaning it automatically corrects what it finds.
How Bugbot Autofix Works
How It Works in a Nutshell
Bugbot could already analyze code changes and leave comments with feedback – much like a colleague would during a review. But it never went beyond comments: you had to make the fixes yourself.
Autofix changes that. When Bugbot finds a problem, it can not only describe it but also launch a separate cloud agent to handle the fix. Simply put: the bot finds a bug, and the bot fixes it, all within an isolated environment without touching the main workflow.
The agent gets the task's context, works with the code, tests the changes, and generates a ready-made fix. The result appears directly in the pull request – all the developer has to do is review the suggestion and accept or reject it.
Why Use an «Agent» Instead of a Simple Edit?
This is a crucial point. When we say «agent», we don't just mean substituting a correct line for an incorrect one. An agent is a process that can reason about the code: understand why there's an error, what needs to be changed, check if the fix will break something else, and run tests.
This is fundamentally different from autofill or static analysis. A static analyzer will say, «Here's a potential problem».An agent will try to understand the issue and propose a solution – just like a human would, only faster and without getting tired.
The cloud aspect here is also intentional: the agent operates in a separate environment, isolated from the developer's machine. This means it can safely run tests without disrupting the current workflow and doesn't risk accidentally breaking anything locally.
«Closing the Loop» – That's the Idea
In their original announcement, Cursor uses the phrase «closing the loop».It's a fitting metaphor for what's happening.
Previously, the cycle looked like this: write code → send for review → get comments → fix → send again. Each step required human intervention. Bugbot Autofix tries to shorten this path: some iterations can be completed automatically, without waiting for someone else's attention or for you to switch context.
For developers, this means fewer interruptions. Minor and obvious problems can be fixed automatically, leaving human attention for more complex decisions.
Why Use AI Agent for Code Fixes
Is It Working Now or Still in Progress?
Bugbot Autofix is a real feature, not just a conceptual announcement. Cursor has already launched it. However, it's important to understand that this is a tool that «suggests» fixes, not one that applies them behind the developer's back. The final say remains with the human – the agent proposes; it doesn't decide.
This is a sensible approach. Full autonomy in coding is another conversation entirely, with a different level of trust and different risks. For now, Autofix works like a very proactive assistant during code review: it doesn't just say, «There's a problem here», but immediately provides a potential solution.
The Idea of Closing the Loop in Code Review
What This Changes for Developers
Putting technical details aside, the essence is this: code review is a time-consuming process, and a significant part of it is spent on relatively repetitive comments. «This edge case isn't handled», «a null check is needed here», «it's better to extract this block».If some of these things can be automated, it frees up time for more important discussions.
On the other hand, it raises the question of trust: how much can you rely on an automatically suggested fix? This largely depends on the agent's quality, how well it understands the specific project context, and whether there is adequate test coverage to verify the change.
Bugbot Autofix doesn't solve all the problems of code review – and, by all appearances, it doesn't claim to. But it does fill a specific niche: finding and immediately suggesting fixes for clear, reproducible problems. This is more modest than «AI will replace code review», but it's also more honest and likely more useful in practice.
We'll see how it catches on in real teams – where codebases are large, context is complex, and the cost of an error is higher than in textbook examples.