I wish there was a reliable way to choke the agents back and prevent them from doing this. Every line of code added is a potential bug, and they overzealously spew pages and pages of code. I've routinely gone through my (hobby) projects and (yes, still with the aid of an LLM) trimmed some 80% of the generated code with barely any loss of functionality.

The cynic in me thinks it's done on purpose to burn more tokens. The pragmatist however just wants full control over the harness and system prompts. I'm sure this could be done away with if we had access to all the knobs and levers.

I attempt to solve most agent problems by treating them as a dumb human.

In this case I would ask for smaller changes and justify every change. Have it look back upon these changes and have it ask itself are they truly justified or can it be simplified.

Over-editing and over-adding... I can find solutions that are just a few lines of code in a single file where AI would change 10 files and add 100s of lines of code. Writing less code is more important than ever. Too much code means more technical debt, a maintainability nightmare and more places where bugs can hide.

My experience is usually the opposite. The code they write is verbose yes, but the diffs are over-minimal. Whenever I see a comment like "Tool X doesn't support Y or has a bug with Z [insert terrible kludge]" and actually fixing the problem in the other file would be very easy, I know it is AI-generated. I suspect there is a bias towards local fixes to reduce token usage.

Yeah I have always felt GPT 5.4 does too much. It is amazing at following instructions precisely but it convinces itself to do a bit too much.

I am surprised Gemini 3.1 Pro is so high up there. I have never managed to make it work reliably so maybe there's some metric not being covered here.

I always described it as over-complicating the code, but doing too much is a better diagnosis.

With the pi-mono coding agent (running local, open models) this works very well:

"Do not modify any code; only describe potential changes."

I often add it to the end when prompting to e.g. review code for potential optimizations or refactor changes.

As mentioned in the article, prompting for minimal changes does help. I find GPT models to be very steerable, but it doesn’t mean much when you take your hands of the wheel. These type of issues should be solved at planning stage.

It feels like a pointless conversation, if no sampler settings (min_p, temperature etc.) mentioned.