3 ways to streamline AI code reviews across your team
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Author: GitHub · Watch on YouTube
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Original language: English
Original video: 3 ways to streamline AI code reviews across your team · Channel: GitHub · 01:31 · Watch on YouTube
Topics: AI agents, Software engineering with AI, Open source and software ecosystems
Summary
Section titled “Summary”AI-generated code can overwhelm developers when reviewing it takes more time than writing it themselves. Growing review backlogs may indicate that a team’s workflows have not been calibrated for agentic work. Bronte recommends improving the information agents receive, keeping pull requests small, and preventing reviews from accumulating. Teams should also reassess how people and agents divide their work as agent use increases.
Key points
Section titled “Key points”- Review all AI-generated code before it reaches production.
- Growing review backlogs can signal that workflows need recalibration for agentic work.
- Improve agents’ inputs, such as acceptance criteria and relevant prior decisions.
- Poorly scoped work can make reviews harder.
- Reduce the scope of agents’ tasks so pull requests are smaller and reviewable in one pass.
- Keeping pull requests small remains an important engineering fundamental.
- GitHub’s stacked PRs feature can help with smaller pull requests.
- Avoid letting reviews pile up.
- As agent use grows, reconsider the roles people and agents play; engineers may spend more time in direct and assess modes than in perform mode.
Chapters
Section titled “Chapters”| Start | Chapter | Summary |
|---|---|---|
| 00:00 | The challenge of reviewing AI-generated code | Christopher describes how the volume of AI-generated code can leave developers overwhelmed by review work. |
| 00:30 | Improve scoping and agent inputs | Bronte explains that backlogs can point to poorly scoped work and recommends improving inputs such as acceptance criteria and prior decisions. |
| 01:01 | Keep pull requests small | Smaller, one-pass-reviewable pull requests apply established engineering fundamentals, and stacked PRs can help. |
| 01:31 | Reconsider the review system | Teams should prevent reviews from piling up and reassess how work is divided as people and agents take on changing roles. |
Quotes
Section titled “Quotes”How should organizations manage reviewing all the code that AI generates? — 00:00
I see growing review backlogs as a signal that the overall system hasn’t yet been calibrated for the newer agentic ways of working. — 00:30
So pull requests are smaller and reviewable in one pass. — 01:01
try to avoid letting those reviews pile up. — 01:31
Concepts and entities
Section titled “Concepts and entities”DevOps, Agentic Engineering System, stacked PRs, GitHub, direct mode, assess mode, perform mode
Full transcript (original language)
Section titled “Full transcript (original language)”00:00 Christopher: How should organizations manage reviewing all the code that AI generates? We understand, of course, the importance of DevOps and reviewing all of that code before it eventually gets pushed out to production. But I talk to developers who often feel overwhelmed by the amount of code, and that sometimes they’re really spending more time reading and reviewing that code than they would have spent if they would have just written it by themselves.
00:30 How can we help streamline that review process? Bronte: I see growing review backlogs as a signal that the overall system hasn’t yet been calibrated for the newer agentic ways of working. Often, one or more of the following might be occurring. First, the work an agent picked up may have been poorly scoped. In our Agentic Engineering System, we would see this as an opportunity to improve the health of shared knowledge, or the direct mode. It’s about improving the inputs,
01:01 for example, acceptance criteria or relevant prior decisions, so the review then becomes easier. Second, reduce the scope of what agents are asked to do. So pull requests are smaller and reviewable in one pass. Those who are familiar with coding best practices will recognize that keeping PRs small is not a new concept. It’s actually a great example of how engineering fundamentals have not changed, and engineers should continue to bring those fundamentals to how they work with agents.
01:31 GitHub’s stacked PRs feature is useful here too. Third, try to avoid letting those reviews pile up. This is about understanding that the system itself needs reconsideration as agents are increasingly used and the modes that people and agents play changes. Whilst output may be increasing, engineers may find they spend more time in the direct and assess modes rather than in the perform mode.