Review the Outcome, Not the Output

The Old Review Model Is Breaking

Code review used to mean reading diffs line by line. You’d check variable names, spot off-by-one errors, argue about formatting, and verify that the logic matched the spec. This made sense when a human wrote every line. If a human chose it, a human should verify it.

But when an AI agent writes the code, line-by-line review stops making sense. That doesn’t mean quality stops mattering — it means the way we verify quality has to evolve. The agent didn’t choose that variable name because it was tired or because it has a bad habit. It followed a pattern. Nitpicking its syntax is like copy-editing a compiler’s output. You can do it. You just shouldn’t.

Output vs. Outcome

There’s a distinction that matters here. The output is the code — the diff, the lines changed, the files created. The outcome is what changed in the system’s behavior. Did the bug get fixed? Does the feature work? Did performance improve? Is the user’s need met?

When you review output, you’re asking “is this code correct?” When you review outcomes, you’re asking “did this change accomplish its goal?” The second question is harder, more valuable, and the one only a human can reliably answer.

An agent can generate twenty different implementations that all pass the tests. Most of them are fine. Some are better than others. But whether the feature should exist at all — whether it solves the right problem, fits the architecture, and serves the user — that’s judgment. That’s yours.

What Outcome Review Looks Like

Outcome review isn’t less rigorous than line-by-line review. It’s differently rigorous. Instead of scanning for syntax errors, you’re evaluating:

• Intent alignment. Does this change match what was actually requested? Agents are good at following instructions literally. They’re bad at questioning whether the instructions were right.
• System impact. How does this change interact with the rest of the codebase? Does it introduce coupling that will hurt later? Does it respect existing boundaries?
• User impact. Does the end result actually improve the experience? A technically correct change that makes the product worse is still a bad change.
• Test coverage. Not “did the agent write tests” but “do these tests verify the thing that matters?” An agent will happily generate tests that pass without testing anything meaningful.

This is the work that requires understanding the business, the users, and the system’s history. No agent has that context. You do.

The Judgment Shift

As AI handles more of the production work, the human role shifts from author to editor, from implementer to evaluator. This is the same shift I explored in Code Was Never the Goal — the craft was always about judgment, not keystrokes. This isn’t a demotion. Editing is harder than writing. Evaluating is harder than implementing. Knowing whether something should be done requires more skill than knowing how to do it.

The engineers who thrive in agentic workflows won’t be the fastest typists or the ones who memorize the most APIs. They’ll be the ones with the best judgment — the ones who can look at a change and know whether it moves the system in the right direction.

Let Go of the Diff

If you’re still reading every line of agent-generated code, you’re spending your attention on the wrong thing. You’re reviewing output when you should be reviewing outcomes. The code is a means to an end. Focus on the end.

This article was originally posted on LinkedIn.