Code review should not be reduced to scanning every changed line for defects. Its lasting value also lies in explaining why a change exists, testing decisions, and helping a team maintain a shared understanding of its software. In a September 30, 2026 article for The New Stack, Ankit Jain argues that teams should automate repeatable checks while protecting the human conversation around judgment and context. The article is sponsored by Aviator, whose cofounder and CEO is Jain; the workflow below is his proposal, not an independently validated standard.
Why code review matters beyond finding defects
Review serves at least two purposes: it can catch problems, and it can help developers understand the system and one another’s decisions. Jain’s argument is that treating review as a line-by-line bug hunt can obscure that second function. If a reviewer sees only a diff, the reasoning and debate behind it may already be missing.
Jain cites a 2013 Microsoft study by Alberto Bacchelli and Christian Bird: as he reports it, 44% of developers ranked finding defects as their top reason for code review, while 14% of 570 classified review comments concerned defects. Those figures describe different things—developers’ stated reasons versus the distribution of comments—and are reported here through Jain’s article, not an independent examination of the original study.
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The practical implication is not that defect detection is unimportant. It is that a review process that measures success only by bugs found risks losing knowledge transfer and shared understanding.
What “review theater” means in the argument
Jain uses “review theater” for a process that looks like review but does not reliably deliver its human benefits: superficial skimming, or a loop in which AI-generated changes receive more automated commentary without clarifying the intent or decisions behind them. He argues that automated systems can apply repeatable checks consistently, while human reviewers are needed to assess context and decide whether the team is building the right thing. That is the article’s position, not proof that every AI system has the same limits.
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The concern is partly about pressure on the process. Jain describes Faros AI data covering 22,000 developers across more than 4,000 teams: incidents per PR rose 242.7%, bugs per developer 54%, work restarts 13.8%, and PRs merged with no human or agentic review 31.3%. These are figures as summarized in his article; the underlying Faros report is not independently verified here, and the article’s account does not establish that AI caused the changes. Jain also characterizes DORA’s 2025 report as finding that AI adoption raises delivery throughput and delivery instability together, without providing a specific figure in the cited article.
A five-layer workflow for keeping the useful parts of review
Jain proposes five layers—Argue, Capture, Codify, Debate, and Own—to move mechanical checks toward automation while keeping decisions and responsibility visible. The sequence is a practical framework, not a controlled evaluation or proven best practice. Teams can adapt it to their workflow.
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1. Argue: compare approaches before implementation
Before opening a pull request, surface plausible approaches and disagreements. Jain suggests using separate agents to generate or critique alternatives, then retaining both proposed and rejected decisions. Agreement among models should not be treated as a final verdict. His examples of tools that can cover parts of this step include PR-Agent, Aider architect mode, AutoGen, and CrewAI.
The useful output is not a pile of generated opinions. It is a concise account of the options considered, the reasons for choosing one, and any question that remains unsettled.
2. Capture: attach intent and decisions to the change
Record the context a reviewer needs alongside the pull request. Include:
- Intent: why the change is being made.
- Acceptance criteria: what observable behavior counts as success.
- Decisions: which trade-offs were made as implementation evolved, including disagreements or unresolved questions.
This gives reviewers a basis for assessing whether the implementation serves its purpose, rather than asking them to infer purpose from code alone.
3. Codify: turn repeatable corrections into guardrails
When the same objective correction recurs, consider making it a checked invariant. Jain’s examples include requiring a Money type for currency and using structured logging. A rule is a good candidate for automation when the expected result is clear and consistent; questions of intent, trade-offs, or suitability still need human judgment.
4. Debate: reserve human review for unsettled questions
Use the review conversation to examine alternatives and decisions that cannot be settled by checking the diff against recorded context. That may mean discussing whether the change meets the stated criteria, whether a trade-off is acceptable, or whether the approach fits the system. The aim is not to eliminate comments, but to make them more consequential than repeated reminders about mechanically checkable rules.
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5. Own: assign responsibility for rules and understanding
Name who maintains the invariants and who is responsible for keeping the team’s understanding of the system current. Automation can enforce a rule, but it does not by itself establish who changes that rule when requirements evolve or who resolves a disagreement about the system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to tell whether a review process is doing its job
Jain does not provide a measured scorecard. His argument suggests asking whether a process supports these outcomes:
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- Reviewers can see the change’s intent and acceptance criteria.
- Alternative approaches and unresolved decisions are visible.
- Human discussion focuses on context and judgment rather than recurring mechanical corrections.
- Someone is accountable for maintaining the rules and system knowledge.
These are diagnostic questions derived from the framework, not reported performance results. A team should not infer that adopting the five layers will automatically improve defect rates or delivery outcomes.
What the proposal does—and does not—establish
Jain’s central recommendation is to build tools for repeatable work while preserving what review contributes through human understanding. He also quotes Addy Osmani’s observation: “We made writing cheap, and understanding stayed exactly as expensive as it has always been.” That captures the tension: producing changes faster does not make their intent or consequences self-explanatory.
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The article offers a sequence and concrete examples, but it does not report a controlled test of the five-layer workflow or independently establish the underlying figures it cites. It is also sponsored by Aviator, and its author, Jain, is Aviator’s cofounder and CEO, as noted in his author profile. That relationship is relevant context for readers; it does not make the proposed practices product claims or evidence of a particular platform’s effectiveness.
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