How should code created by AI coding agents be reviewed?

Instead of reading the diff line by line, you should first review the work report left by the agent. The report should include the task's goals, constraints, considered alternatives, decision rationale, and verification results. Based on this context, people then selectively check only the key changes.

What is the difference between diff review and report review?

Diff review only checks the correctness of the code changes themselves, while report review also verifies the context of why the agent made those decisions. Report review reduces misunderstandings and can better identify security and performance issues.

How do you record an agent's decision rationale?

You can require the agent to write a structured report such as 'goal-constraints-decision-verification' whenever it creates a PR. You should record the considered alternatives, why they were abandoned, and the trade-offs so that it can later be used as an onboarding asset.

What are the benefits of accumulating reviews as versions?

When review records are stacked as immutable versions, you avoid repeating the same mistakes and can trace past judgments. When a problem occurs, you can trace back which review approved it and on what grounds, making accountability clear.

What is the role of human reviewers in the AI coding era?

Human reviewers move away from verifying code line by line to judging the validity of the agent's work context and decisions. Review becomes an activity that accumulates the team's knowledge assets rather than a simple approval.

How the Way People Review Changes in the Age of AI Coding Agents

md-logOctober 24, 20266 min read

In an era when agents pour out code, people must review the context of the work, not the diff. This covers the shift to leaving decision rationale as reports and accumulating reviews as versions.

Recently, as the volume of code produced by AI coding agents has overwhelmed the speed of human review, the existing diff-centric review has hit its limits. Now, the focus of review must shift from 'Is this line of code correct?' to context review that verifies 'Why did the agent perform this task in this way?' By leaving decision rationale in report form and accumulating reviews themselves as versions, onboarding assets and collaboration history build up together.

Agent Output Creates a Review Bottleneck

Recently, there have been reports that AI agents are beginning to make autonomous decisions in shopping, advertising, and more. In October 2026, The New York Times and CNBC covered the trend of AI agents making purchasing decisions on behalf of consumers, and ADWEEK emphasized explainable logic and responsible execution. Coding is no exception. AI coding agents generate in minutes amounts of code that would be difficult for a person to write in a day. The problem is that all of that code must pass review.

Traditional code review reads changed diffs line by line to check correctness, style, and potential bugs. But when an agent pours thousands of lines of diff into a single PR, it is practically impossible for a human reviewer to read them the same way. For example, if a team where 10 developers each review 500 lines a day has an agent generating an additional 5,000 lines per day, the review queue explodes and eventually people approve with their eyes closed. This leads not only to lower productivity but directly to code quality issues and security vulnerabilities. Agent output creates a review bottleneck, and the bottleneck in turn degrades verification quality—a vicious cycle.

The Limits of Diff Review and the Shift to 'Report Review'

The essential limitation of diff review is that it shows 'what changed' but not 'why it changed.' Code generated by an agent is usually the result of multiple stages of reasoning and exploring alternatives, and looking only at the diff erases that context. For example, suppose an agent introduced caching in a particular module for performance optimization. The diff alone shows that cache code was added, but you cannot tell why the agent identified this point as a bottleneck or which alternatives (e.g., index changes, query rewriting) it considered. As a result, the reviewer may mistake it for unnecessary complexity, or conversely miss an important security issue.

Therefore, the unit of review must shift from diff to a 'work report.' Report review is a method of examining together the goal, constraints, alternatives considered, final decision rationale, and test results that the agent left before and after the task. This is in line with recent discussions in e-commerce that controlling AI website agents requires clear rules and audit logs. In practice, it is effective to design prompts so that when an agent creates a PR, the body must include four items: 'goal-constraints-decision-verification.' Instead of reading the code diff from the beginning, a human reviewer first reads this report, judges the legitimacy of the task, and then selectively checks only the key changes.

Recording Decision Rationale Becomes an Onboarding Asset

The report left by an agent is not a one-off document for that moment's review. Over time, it accumulates as the team's onboarding asset. When a new developer joins, the biggest difficulty is not the codebase itself but the tacit knowledge of 'why is it designed this way?' For example, if a new team member asks, 'Why does this authentication module use sessions instead of JWT?', and the related PR report includes a record such as 'At the time, we chose sessions because of the requirement to limit simultaneous logins across multiple devices, and we excluded JWT because managing revoked tokens was complex,' they can find the answer immediately.

Just as recent trends emphasize AI agents explaining the rationale for their choices in shopping recommendations, code agents must also leave structured decision rationale. Not simply a summary like 'fixed something,' but records that include considered alternatives, why those alternatives were abandoned, and trade-offs, so that onboarding value emerges. Decision records accumulated this way become a 'living document' that is more specific and contextual than code comments or a wiki.

Accumulating Reviews as Versions Builds Collaboration History

A review is not a one-time event that ends after checking, but should be accumulated as the team's knowledge asset. When review records are stacked as immutable versions, the same mistakes are not repeated and past judgments can be traced. For example, if a previous review pointed out a specific security vulnerability pattern (e.g., string concatenation with SQL injection risk), then when the agent later generates similar code, the reviewer can refer to the previous review and judge quickly. Also, when an agent's decision later causes a problem, it is possible to trace back which review approved it and on what grounds, making accountability clear.

Similar to how e-commerce companies stack rules by version to control AI website agents, code reviews also need versions accumulated step by step to become collaboration history. Especially in distributed teams or asynchronous collaboration environments, it is important that review comments remain in a single timeline rather than being scattered. Records of 'who, when, and why' approved something reduce the cost for the next person to understand the context.

Time to Shift to Review That Verifies Context

As the amount of code generated by AI coding agents grows, the way humans read every diff is not sustainable. Now the essence of review must shift from 'Is the code correct?' to verifying whether the agent performed the task in the right context. We need tools that allow humans to review the decision reports left by agents and help those reviews accumulate as versions. If you have a layer like md-log, where people can comfortably review AI-written work and analysis on web, phone, or tablet, and where every save is stacked as an immutable version to leave collaboration history, you can reduce the review bottleneck while naturally accumulating the team's onboarding assets and collaboration records.

References

#AI coding #code review #context review #report review #decision records #collaboration history

Frequently asked questions

How should code created by AI coding agents be reviewed?: Instead of reading the diff line by line, you should first review the work report left by the agent. The report should include the task's goals, constraints, considered alternatives, decision rationale, and verification results. Based on this context, people then selectively check only the key changes.
What is the difference between diff review and report review?: Diff review only checks the correctness of the code changes themselves, while report review also verifies the context of why the agent made those decisions. Report review reduces misunderstandings and can better identify security and performance issues.
How do you record an agent's decision rationale?: You can require the agent to write a structured report such as 'goal-constraints-decision-verification' whenever it creates a PR. You should record the considered alternatives, why they were abandoned, and the trade-offs so that it can later be used as an onboarding asset.
What are the benefits of accumulating reviews as versions?: When review records are stacked as immutable versions, you avoid repeating the same mistakes and can trace past judgments. When a problem occurs, you can trace back which review approved it and on what grounds, making accountability clear.
What is the role of human reviewers in the AI coding era?: Human reviewers move away from verifying code line by line to judging the validity of the agent's work context and decisions. Review becomes an activity that accumulates the team's knowledge assets rather than a simple approval.

← All posts