CODE REVIEW AT AI SPEED

Integrate AI in code review: use AI for meticulous checks, automate changelogs, and separate tasks among coding agents

Jan 15, 2026 7m 25s

Maciej Korolik

Expert Insights

Maciej Korolik articulates the integral role of AI in enhancing the code review process, asserting that it's not merely a substitute but an essential third pair of eyes. He promotes a shift in mindset from AI as an assistant to AI as an essential team member, underscoring the crucial role of AI in keeping pace with our ever-increasing scale of code.

Maciej expands on the capabilities of AI to overcome human limitations, particularly in meticulous tasks prone to error due to factors such as fatigue or the bulk of files. Furthermore, he highlights the importance of contextual knowledge in AI and its positive implications for shaping future code development.

Hear Maciej explain:

The role of AI in code review not just as a redundancy but an integral team member.
The emergence of AI as an essential tool in mitigating human limitations in code reviews.
How AI, through the extraction of historical context and building upon it, can lead to more intelligent coding practices.
The significance of separating tasks among different AI coding agents to ensure comprehensive and unbiased code reviews.
The crucial role of AI review agents in solo developer scenarios providing a crucial check and balance in code review.

We use a tool called Code Rabbit. It connects to our GitHub. The tool is made specifically to review the code and try to find the issues. It's actually important to separate the different tasks between different coding agents. So, each time I do some new feature, I add it in the change log. And we get it almost for free with AI. When I push my new changes, Code Rabbit will trigger the review again. It's very important because when I use one agent in Cursor to build the plan, build the code, and then I ask same agent for a review, there's a big probability that it'll say that everything is nice. However, when I use a separate tool that doesn't have the context, it's trained to be very critical.

Monterail Team Analysis

Here's how software teams can leverage AI in the code review process to maximise efficiency and reduce human error:

Adopt tools like Code Rabbit: Use AI software that is purpose-built for code review, ensuring comprehensive and unbiased reviews.
Integrate AI as an integral part of the team: Reframe your perception of AI from an assistant to becoming an important component of your coding team, being an extra set of eyes on the process.
Leverage AI in maintaining effective changelogs: Utilise AI in documenting important changes and decision history, thus relieving developers from this task and ensuring more effective record keeping.
Separate tasks between different AI agents: To ensure an unbiased review process, use different AI tools for different stages of the coding process. One to develop the code, and another to review the code.
Make the most of AI in solo developer cases: For smaller projects or solo developers, using AI for code review becomes crucial as it provides a necessary check and balance in code review.
Benefit from the continual learning ability of AI: Feed the historical context, and changes made in prior reviews, back into the AI system to enhance its future performances.
Regularly refresh the context: To get the best out of AI when it comes to code review, update the AI with changes in libraries, methodologies, and other context to maintain its effectiveness.