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Comment by MarcLore | Hacker News Reader

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My experiments using a more hands off approach of prompting claude code have always resulted in a two-steps-forward-one-step-back play, where the agent clearly did some good stuff, but did some other stuff in a somewhat undesirable manner, which subsequently needed correcting.

This usually results in A: creating commits where tons of code is being constantly added and removed, B: due to Claude's somewhat cavalier attitude to existing code, has steadily eroded my familiarity with the code base.

I'm still not convinced that these longer loops are that beneficial, compared to 1min prompts to 5-10min AI work.

The add/remove churn is the thing nobody talks about enough. We track net lines changed vs gross lines changed as a ratio and it's genuinely horrifying sometimes. An agent will touch 400 lines to net 30 lines of actual progress. And the worst part is the removed code was often fine, the agent just decided to refactor something it didn't need to touch while it was in there. Shorter, tighter loops with clearer scope boundaries have helped us more than any orchestration improvement.

The most horrible thing I have seen with AI in coding is that it tends to add vestigial bits of code for no reason - this or that class that nobody uses at no point in the commit history, or stuff that gets added, then we decide to implement the feature in a different way, but parts of the old implementation stick around.

Doesn't delegating make this a lot more possible? You can fire off a request to a sub-agent, they respond with some predictable status that you can parse, and then you continue (you being the "main" agent), so the context window can remain relatively small. Kinda like how a human does it.

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