Fixing the PR Bottleneck
Pull requests have always been a bottleneck, and today the situation has become even worse. Agents generate changes much faster than humans can review them.
Let's check what Matt Pocock recommends doing about that in his new talk Fixing the PR Bottleneck.
First of all, he suggests using a three-layer code review approach:
1. Automated checks: linting, type checking, testing.
2. Automated review: agents that enforce coding standards.
3. Human review.
The main goal is to make human review faster by relying on the first two layers. And to make automated reviews effective, you need to teach your agents your codebase design rules: how the code is structured, how to organize modules, how to write tests properly, etc.
Some recommendations on how to organize the process:
🔸 Keep codebase design rules separate. Describe them in a CODING_STANDARD.md file, not in AGENTS.md, and run the review in a separate session.
🔸 Don't outsource automated code review. Generic code review doesn't work well in practice. It produces too many false positives.
🔸 Automated reviewers should fix issues themselves. Agent should not create review comments on PRs and expect human to read them, it should provide fixes before human review.
🔸 Some PRs are more important than others. Categorize them by blast radius and include this information in the PR.
🔸 Run retrospectives regularly. You shouldn't have to leave the same comment twice. Teach your agents and incorporate this knowledge into your coding standards.
There is definitely a lot to think about how modern code review should be organized.
I personally liked the idea of having a code review agent fix issues instead of leaving comments for humans to handle. Anyway an engineer will just ask another agent to fix them first. So there is no need for additional human intervention here. I'll definitely try this in my teams.
Also, Matt has a pretty popular GitHub repository with skills. There are a lot of useful engineering skills there that you can try out with your own team.
#ai #engineering #codereview
Pull requests have always been a bottleneck, and today the situation has become even worse. Agents generate changes much faster than humans can review them.
Let's check what Matt Pocock recommends doing about that in his new talk Fixing the PR Bottleneck.
First of all, he suggests using a three-layer code review approach:
1. Automated checks: linting, type checking, testing.
2. Automated review: agents that enforce coding standards.
3. Human review.
The main goal is to make human review faster by relying on the first two layers. And to make automated reviews effective, you need to teach your agents your codebase design rules: how the code is structured, how to organize modules, how to write tests properly, etc.
Some recommendations on how to organize the process:
🔸 Keep codebase design rules separate. Describe them in a CODING_STANDARD.md file, not in AGENTS.md, and run the review in a separate session.
🔸 Don't outsource automated code review. Generic code review doesn't work well in practice. It produces too many false positives.
🔸 Automated reviewers should fix issues themselves. Agent should not create review comments on PRs and expect human to read them, it should provide fixes before human review.
🔸 Some PRs are more important than others. Categorize them by blast radius and include this information in the PR.
🔸 Run retrospectives regularly. You shouldn't have to leave the same comment twice. Teach your agents and incorporate this knowledge into your coding standards.
There is definitely a lot to think about how modern code review should be organized.
I personally liked the idea of having a code review agent fix issues instead of leaving comments for humans to handle. Anyway an engineer will just ask another agent to fix them first. So there is no need for additional human intervention here. I'll definitely try this in my teams.
Also, Matt has a pretty popular GitHub repository with skills. There are a lot of useful engineering skills there that you can try out with your own team.
#ai #engineering #codereview