
Development teams are seeing a rapid increase in code contributions, many of them generated with artificial intelligence (AI) tools, including pair-programming copilots. As the volume of AI-generated code increases, human reviewers can quickly become overwhelmed.
The instinct to throw “an AI reviewer” at the problem often does not work. A single agent produces shallow comments, misses context, and flags either too many or too few issues, depending on how it is prompted.
In this talk, Vikram Vaswani presents a more effective pattern, built and demonstrated from scratch: three specialised review agents working in parallel, followed by a developer agent that applies the fixes. One reviewer focuses on architecture, one on security, and one on test coverage. When all three finish, a fourth agent rewrites the code to address each issue and opens a new pull request (PR) for human sign-off.
Attendees will leave with a concrete, working pattern they can adapt to their own projects and a clearer understanding of where AI can genuinely reduce review bottlenecks.
Benefits
Outline
The architecture
Context
This situation is real. For example, tldraw and Ghostty have stopped accepting contributions due to the high volume of AI slop. For additional context, the session references “Stop Closing the Door. Fix the House,” a blog post by Angie Jones, a maintainer of Block’s goose project, which has more than 300 external contributors:
https://lnkd.in/exquiTP6?trk=public_post_reshare-text
The session also references Kelsey Hightower’s comment on the topic:
https://www.linkedin.com/posts/kelsey-hightower-849b342b1_stop-closing-the-door-fix-the-house-angie-activity-7425980144877555712-Uedv