How to review AI code without reading the code

As AI tools increasingly handle line-by-line pull request checks, Ankit Jain argues that teams risk losing code review’s more valuable function: building shared understanding and debating technical and product decisions. He proposes a five-layer framework in which agents challenge proposed approaches, capture intent and acceptance criteria, and codify recurring review feedback as automated invariants, while people debate unresolved trade-offs and assume ownership of final decisions. Rather than relying on upfront specifications that may quickly drift, the approach records decisions made during AI-assisted implementation, including rejected alternatives, accepted risks, and follow-up work. Reviewers would focus on understanding a change’s purpose, discussing key decisions, and checking failures, consulting code only when needed. Jain also recommends preserving decision records after merging so teams and future AI agents can reuse past reasoning, convert repeated choices into approved rules, and maintain clear human accountability for system outcomes.

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