Why Reviewing Every Line of AI-Generated Code Falls Short

Coding agents can now write, refactor, and ship substantial chunks of software with little direct human typing. That shift raises a practical question for developers: if you didn't write the code yourself, how do you know it actually works?

A common instinct is to read every line an agent produces, the same way you'd review a colleague's pull request. Simon Willison argues that instinct is misplaced. Line-by-line reading does not scale to the volume and pace at which coding agents generate output, and it doesn't reliably catch the kinds of mistakes that matter most.

Verification Over Inspection

The more effective approach, Willison suggests, centers on two distinct skills. The first is giving an agent clear, specific instructions about what change to make and why. Vague prompts produce vague, hard-to-verify results, while precise instructions make the agent's output easier to check against a known intent.

The second skill is verification itself, but not the kind done by eye. Running tests, checking that a feature behaves correctly in practice, inspecting diffs for scope rather than syntax, and confirming that nothing unrelated broke all do more to establish trust in a change than a manual read-through of every modified line.

Eyeballing every line of code is not the most effective way to validate changes to software.

This reframes code review for an agent-driven workflow. Traditional review exists partly to catch bugs and partly to transfer understanding between human collaborators. When the "collaborator" is an agent that can generate code far faster than any person can read it, the bottleneck shifts from writing to confirming correctness at the level of behavior, not text.

Why This Matters for Teams Adopting Agents

Developers who keep trying to manually audit every agent-produced line risk becoming the slowest part of their own workflow, without actually catching more defects. Treating verification as a distinct discipline, backed by tests, runtime checks, and clear upfront specification, offers a more durable way to work with agents at scale.

For teams integrating coding agents into daily practice, the practical takeaway is to invest less in exhaustive manual reading and more in building verification habits: strong test coverage, clear task framing, and deliberate checks on agent output before it merges. That combination, rather than closer reading, is what makes fast agent-assisted development trustworthy.