Comment by deaux
6 days ago
Thank you for turning up here and replying!
> The LLM-generated prompts follow the coding agent recommendations. We also show an ablation over different prompt types, and none have consistently better performance.
I think the coding agent recommended LLM-generated AGENTS.md files are almost without exception really bad. Because the AGENTS.md, to perform well, needs to point out the _non_-obvious. Every single LLM-generated AGENTS.md I've seen - including by certain vendors who at one point in time out-of-the-box included automatic AGENTS.md generation - wrote about the obvious things! The literal opposite of what you want. Indeed a complete and utter waste of tokens that does nothing but induce context rot.
I believe this is because creating a good one consumes a massive amount of resources and some engineering for any non-trivial codebase. You'd need multiple full-context iterations, and a large number of thinking tokens.
On top of that, and I've said this elsewhere, most of the best stuff to put in AGENTS.md is things that can't be inferred from the repo. Things like "Is this intentional?", "Why is this the case?" and so on. Obviously, the LLM nor a new-to-the-project human could know this or add them to the file. And the gains from this are also hard to capture by your performance metric, because they're not really about the solving of issues, they're often about direction, or about the how rather than the what.
As for the extra tokens, the right AGENTS.md can save lots of tokens, but it requires thinking hard about them. Which system/business logic would take the agent 5 different file reads to properly understand, but can we summarize in 3 sentences?
Yes that's a great summary and I agree broadly.
Note with different prompt types I refer to different types of meta-prompts to generate the AGENTS.md. All of these are quite useless. Some additional experiments not in the paper showed that other automated approaches are also useless ("memory" creating methods, broadly speaking).
I will read the paper, but I am curious if the methods promoted by eng/researchers at openai for models like codex 5.2/5.3 work? ie, is having a separate agent look at recent agent sessions and deduce problems the agents ran into and update agents.md (or more likely, the indexed docs referenced in an agents.md) actually helpful? A priori that seems like the main kind of meta prompting/harness you might expect to work more robustly.