When an AI agent writes the code, reviewing its output is still essential—but it is not enough. The more useful shift is to define the requirements and architecture before generation, then verify both the result and its fit with the surrounding system. That is the central lesson of an essay reflecting on 30 days of AI-generated software: the engineer’s work moves upstream into specification and design, and remains downstream in testing and integration.
What changes when an AI agent writes the implementation?
Traditional code review asks whether another developer’s implementation is correct, maintainable, and consistent with the project. With an agent, the engineer also has to decide what the implementation must do and describe those expectations clearly before code exists. If the task leaves important requirements or architectural choices open, reviewing the generated code afterward may not reveal that the right problem was never specified.
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The essay presents this as the author’s experience, not as a controlled study or a universal result. Its author describes twelve years of code review and thirty days using AI-generated code as the primary workflow. Those are autobiographical time spans; the essay supplies no measured reliability rate or controlled comparison.
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Specify the work before generation
Treat a prompt as a working contract, not a one-line feature request. State the behavior the code must provide, the boundaries it must respect, and how success will be checked. The essay specifically recommends making constraints, naming conventions, error-handling boundaries, test requirements, and interfaces explicit.
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Define the interface first
Where practical, settle the interface before asking the agent to implement it. Specify inputs, outputs, expected errors, and how the new code connects to existing components. This gives the agent a bounded implementation task and gives the reviewer concrete criteria for checking the result.
Turn a feature ticket into acceptance criteria
Before generation, clarify what should happen in ordinary cases, what should happen when something fails, and which existing project conventions matter. If an architectural choice is not already settled, make it an explicit decision rather than leaving the agent to infer it silently.
Keep agent tasks small and preserve project context
Large tasks make it harder to keep requirements, architecture, and implementation aligned. The essay recommends dividing work into smaller units and maintaining a living CONTEXT.md file with decisions, conventions, and known constraints that the agent needs. Keep that file current as the project changes; stale context can be as misleading as missing context.
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Verify behavior and integration, not just readable code
Generated code can look plausible without meeting the specification or fitting the larger system. The essay’s verification advice has two parts: check whether the code conforms to the request, and check whether it works with its dependencies and surrounding architecture.
Test against the requested behavior
Write or identify tests from the expected behavior before reading the implementation in detail. Tests can expose a mismatch between what was requested and what was generated, even when the code appears polished. They are an early check, not proof that the implementation is correct in every respect.
Trace dependencies through the system
Reason from the relevant system boundaries and dependencies toward the generated change. Check how callers, data, errors, and adjacent components interact with it. A unit that satisfies its local interface can still fail when integrated into the actual application.
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Review remains a quality-assurance layer. Inspect the implementation for correctness, maintainability, and unintended side effects after checking its stated behavior and integration. The essay’s point is not to replace review with prompting; it is to avoid treating review as a substitute for clear specification and design ownership.
Keep architectural ownership with the engineer
The essay recommends that humans own design and architectural decisions while agents handle implementation. When an agent makes a non-obvious decision, ask it to explain the rationale and consider alternatives. Evaluate that reasoning against the project’s requirements and constraints; an explanation is something to assess, not automatic evidence that the decision is sound.
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Keeping prompts and agent conversation logs can also preserve the rationale behind a generated change. They help connect implementation choices to the requirements and decisions that led to them, making later maintenance and review less dependent on reconstructing that history.
What this means for someone whose job is code review
Keep reviewing, but practice moving part of the work earlier. Take a feature ticket and turn it into a precise specification: define the interface, constraints, error behavior, and tests; then evaluate whether the generated implementation meets those requirements and integrates correctly. This is a practical way to build the specification and design skills the essay argues become more important in an agent-assisted workflow.
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Should I stop reviewing code altogether and focus only on prompting?
No. Keep code review as a quality-assurance step. Clear prompting and specification make the task better defined, but they do not replace checking correctness and integration.
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How do I know when an AI agent has made a good architectural decision versus a plausible-looking bad one?
Ask for the rationale and alternatives for non-obvious decisions, then evaluate them against your project’s requirements, constraints, and architecture. The agent’s explanation does not replace human judgment.
What’s the practical takeaway for someone whose day job is code review today?
Practice turning feature tickets into precise specifications with explicit interfaces, constraints, error behavior, and tests, then verify the generated implementation against them.
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