AI models that over-edit code have a prompting problem, not an AI problem.
There's a piece getting traction on HN right now about "minimal editing" — the observation that AI coding tools routinely change far more code than the task requires. The comment section is full of engineers nodding along.
We've lived this. Early in building Scarlet, our internal AI dev toolkit, we'd hand an agent a task and get back three refactored files when we asked for one bug fix.
The fix wasn't to limit the AI. It was to get sharper at directing it.
What actually changed:
— Tasks scoped to single responsibilities, not vague objectives
— Explicit constraints in every prompt ("only modify this function", "do not refactor")
— Review steps baked into the agentic loop before any write operation
— Agents that audit their own diffs before committing — not just generate and move on
Over-editing is what happens when you treat AI like a junior dev you throw work at. It isn't that. It's a system you architect.
When we ship a full-stack platform in 15 working days, it's not because agents run loose. It's because scope is tight, review is automatic, and each step in the pipeline knows exactly where its job ends.
Less AI autonomy isn't the answer. Better workflow design is.
Originally posted on LinkedIn — follow us for daily AI engineering tips.
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