Does code cleanliness affect coding agents? A controlled minimal-pair study

Interesting question to study, but I'm extremely skeptical of the experimental design. They used Opus 4.6 to synthetically produce "degraded" or "cleaned" code bases for relative comparison in the experiment.

Worse, they don't control for breaking the application's tests.

> Pass rate scores the agent’s final state against the hidden tests we wrote for each task. We do not check whether the agent broke unrelated tests already present in the repository, and a cleaner-side and messier-side solution that both pass the hidden test may still differ on tests they were not graded on.

Any conclusions with respect to token consumption seems pretty meaningless if we're not controlling for the quality of the final output.

One trick I've found that works well is to tell it to refactor, e.g for Python:

    Refactor the Python code to make it more Pythonic, e.g. fewer classes/singletons, especially if it will provide a speedup. The Python code **MUST** follow code organization standards expected of popular open-source Python packages code without causing any benchmark performance regressions.

A variant I've used for Rust code:

    The Rust codebase in `/src` has become bloated with several files >1k LoC. Refactor the Rust codebase to fit code organization standards expected of popular open-source Rust code without causing any benchmark performance regressions.

Those types of prompts appear to a) reorganize the code logically and b) do seem to get better performance from the agents because the file names now provide semantic hints to where relevant code resides. For bloated 5k LoC files, the agent has to Read several chunks to find relevant code which is inefficient.

In terms of benchmark performance it generally improves after the refactor which I suspect is coincidental (especially in Rust where it shouldn't matter due to compiling) but I'm not complaining.

In my experience, the delta in agent performance is substantial if the codebase is littered with dead code, redundant code, unreachable fallbacks, leaking abstractions and half-baked design patterns vs if the code is well-organized, with clear data flow, with good encapsulation and clean architecture. Like, I've seen all the frontier models have to do several rounds of code review / QA and fix when the code is bad vs just getting it right at the 1st/2nd attempt.

If the models are quantifiably different on cleaner codebases, surely this could be leveraged into a measure of code cleanliness.

I'm not sure if you would call it an objective or subjective measure, a fixed model would be consistent which would provide an objective base for comparison, but other models would be different, so it would be subjective in relation to the model itself.

Even if agents can learn to navigate all the stubs and WET crap they leave behind do we really want a code base that no human can follow what's actually going on?

I can't imagine how it wouldn't. None of them can fit a real codebase in context and have to browse the code the same way a person would. Doing searches and reading files. If the files are in the places they would be expected to be and things are called what the model or a person would first guess to search then it gets found in the first attempt rather than requiring a deep search and multiple attempts.

"agent pipelines that [...] clean a messy [repository]"

This feels like a terrible approach, sufficient to condemn the entire study.

Apparently half of the "minimal pairs" in this work were constructed in this way. I simply am not going to trust any conclusion that requires assuming these AI "cleaned" repos are in any way representative of actually-good codebases.

Agents struggle with this: DRY means they create helper functions and classes for a testing scenario and maybe it's called two times. When you ask for a refactor of your code and tests to follow, these helper functions are also ignored and then, dead code starts piling up. Specific cleanup sessions always seem to leave residues behind and doing Ralph loops in the first place seems to help with this, but I'm just not satisfied with the overall performance. Any ideas?

In my experience everything that affects engineers affects an agent. Good abstractions, reasonably sized methods, good names, principled (intra & inter) service architecture, unit tests, etc.

All of these things have historically been the job of engineers, because it helps other people contribute to the code.

Now it helps other people and other agents contribute to the code.

Another consideration: written by hand, the trade-off of development velocity vs well organised code means that it can be worth taking some tech debt now in order to deliver some value now. (Especially when prototyping etc.).

With coding agents, agents can produce code quicker. The same trade-off still applies.. but, the time it takes an LLM coding agent to write well organised code is still going to be quicker than the time it takes me to write scrappy code.

agents are pretty good at cleaning up a codebase, finding dead code, fixing bad abstractions, etc. You just have to spend some focusing the agents on that goal.

"Across 660 trials with Claude Code, code cleanliness does not change the agent's pass rate. However, it substantially alters the agent's operational footprint: agents working on cleaner code use 7 to 8% fewer tokens and reduce file revisitations by 34%. Our findings suggest that traditional maintainability principles remain highly relevant in the era of AI-driven development [...]"