> Verifying quality is orders of magnitude harder than "did the tests pass"

Agree that agentic grading is the future here. Cognition's Frontier Code is probably the best large public benchmark at this. You attribute agent quality issues to RLVR's binary pass/fail, however I wouldn't be surprised if labs are already supplementing that with rubrics as rewards to train more 'tasteful' models like Fable.

What can a practitioner do? I think there's promise in turning the optimization machine to the harness itself - building out a representative dataset of tasks on your repo, grading agent quality on them across various configurations, and optimizing [AGENTS.md / SKILLS.md / workflow / model / harness / tools] on that signal. High quality grading is still very hard, but it's more tractable at smaller, repo-level scale, and you can afford slower, more expensive verification for each task. You only need it to be right about your codebase's standards.

> In fact, it's not hard to imagine that if a model could reliably tell good code from bad, it might have written the good version to begin with

Pushing back slightly - detecting slop and discriminating quality is easier than generating it (why code review is so effective), and why grading is viable at repo eval scale even if it's much harder at RL scale.

Everyone is flying blind. For example, I am genuinely interested in trying HumanLayer, but would likely want some harder evidence (beyond anecdotes) that it's actually making my agents more effective before rolling out to an enterprise team.

I'm building this harness optimization loop @ https://stet.sh if curious

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In my case, I'm trying to measure how coding agents perform under realistic scenarios when implementing tasks, as a proxy for how agents perform when used by actual users for those same tasks, so it's important to ensure the agents are behaving realistically instead of "cheating" and looking up answers.

Happy to share resources! I've been pretty deep in the space :)

see https://cursor.com/blog/reward-hacking-coding-benchmarks

At a high level, it

- Mines tasks from your merged PRs/commits - Replays them in Docker containers with different harness settings (change model / reasoning effort / AGENTS.md / etc) - Grades the patches on various attributes (tests, equivalence with human patch, code quality)

The goal is to get a sense of how agents perform on your tasks, with your context, using the tools you do.

This is currently one-shot but I'd definitely like to explore session-based benchmarks as well. There are some interesting papers that just came out on this https://arxiv.org/abs/2606.29957 https://arxiv.org/abs/2606.30573

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Personally, I will continue seeking out high-quality music/art/movies/books that speak to me, and most of my friends do the same. There will always be a demand for human-created art, regardless of any plagiarism or replication by labs.

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This is also generally where I've landed - keep the AGENTS.md super light, and link out to docs as needed. Same idea with skills as well. Basically, preserve the context window at all costs.

The part I'm curious about is, when we're making the sorts of behavior changes you're describing on shared repos, how do we actually measure and quantify impact? It's one thing to tell the team that the agent should perform better, and it's another to say that you made the agent 5% better across a variety of tasks for every dev in the repo.

This has been bothering me for a while - the entire dev community is running on vibes when talking about AI. We're operating in an old paradigm, thinking that smart and logical additions to AGENTS.md result in good agent behavior, when in fact agents behavior is such a black box, that measurement is necessary.

> Even when all the rigging is controlled. (Which implies we need multiple experiments to compare against.)

Even the rigging is hard to control - Anthropic has an interesting piece on this here https://www.anthropic.com/engineering/infrastructure-noise

I'm also noticing similar patterns with needing to update AGENTS.md / skills per model release. E.g with Opus 4.6 -> 4.7, it became much more instruction adherent, so instructions written for the prior model generation might cause unexpected behavior in the new generation. I'm also convinced that an optimal AGENTS.md for Codex is not the same file as an optimized CLAUDE.md for Claude - the model personalities and behaviors are so different that we probably need to tune the instructions differently as well.

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- Keep it concise, use progressive disclosure / nested AGENTS.md for information expansion - Give agent the high level repo structure if necessary - Have a "why" section to align the agent, high level, what your code is doing - Keep behavior instructions positive where possible, eg Always clarify intent before acting

For example, if I write a bad AGENTS.md for a repo with 100 engineers actively working in it, then every agent for every engineer gets worse, without anyone really noticing.

I think we should move towards data-based tuning of AGENTS.md, testing out changes, gathering data, and then making a decision on whether or not to ship it.