this post was submitted on 28 Sep 2026
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To quote the research paper conclusion...
Conclusion We evaluate the impact of context files on coding agent performance for four common coding agents on SWE-BENCH and the novel CTXBENCH, built from recent GitHub issues and less popular repositories containing developer-written context files. We find that all context files consistently increase the cost and number of steps required to complete tasks. LLM-generated context files have a marginal negative effect on task success rates, while developer-written ones provide a marginal performance gain, neither statistically significant. Our trace analyses show that instructions in context files are generally followed and lead to more test- ing and broader exploration; however, they do not function as effective repository overviews. Over- all, our results suggest that context files don’t improve coding agent performance, and should only contain specific additional instructions beyond what is already available in the codebase. This high- lights a concrete gap between current agent-developer recommendations and observed outcomes, and motivates future work on principled ways to automatically generate concise, task-relevant guid- ance for coding agents.
That sounds like "Agents.MD doesn't work" to me.
Am I missing something?
your research paper:
thread article:
benchmarks notoriously don't measure the likelihood that the results would be merged by maintainers, just that they passed a test
the old style "repo map" agents.md files that every tool used to create is no longer useful, but of course things that would otherwise be repeated prompts are still good. I cut the ones at work down from hundreds of lines each to tens. without those last few lines the amount of iteration I do - both with agent and when reviewing PRs - goes up significantly
Agents.md doesn't exist to make sure generated code functions, it's there to make sure it's written well.
What is the difference between the models writing code "well" and their performance in this context? Are we referring to readability?
Genuine question. If we use agents to read, edit, and review code, why do we care about readability? That's a human constraint. Unless attempting to do those three is not effective and thus requires human attention to correct issues which would justify readable code. If that's the case; why use the agent to edit the code in the first place?
"Writing code well" includes several relavant things:
As far as I'm aware you do currently still need a human to ensure stuff like this is followed. People use agents because they don't care about the above, or because they can get close enough and intervene to fix any issues that appear.
Agents for generation, humans for review
One, that sounds truly miserable.
Two, there is a decent body of evidence to suggest that this method does not actually speed up development unless you go full lights out software factory which... Why would you want to do that?
https://ide.mit.edu/insights/ai-productivity-and-roi/
EDIT: As an aside this actually reminds me of the Xerox park study into efficiency gains for keyboard heavy workflows vice mouse heavy workflows. Keyboards were perceived as faster by subjects but when actually measured the mouse was faster.
That's just because I need to stop and appreciate my efficiency for 10 seconds every time it saves me 10 seconds :-)
You joke but that's basically the conclusion of both the newer agent studies and the older Xerox park study on peripheral use. Language and logic are stored in an easier to access location in the brain than the kinds of skills used in architecture and planning.
To be clear, I'm no rube. I recognize that these are powerful tools. But I suppose my take here is that if an LLM is necessary to get rid of a lot of the boilerplate and setup for a task that indicates we should explore how we're doing the task.
What I want to see is using these tools not to delegate our problem solving but finding ways to enhance and accelerate it and I don't think writing spec sheets is the answer.
I think you're correct, the amount of rigor in your prompts seems orthogonal from whether you're using them for architecture or implementation.