I am working on a unified theory in https://zby.github.io/commonplace/ - it is all agent edited so it might be hard to read, but hopefully we are catching most logical errors. Some day the llm prose will improve.
I have even a preliminary theory on what is needed for the positive feedback loop: https://zby.github.io/commonplace/articles/reflective-self-i... - (this is not stable yet).
I just got done asking gpt5.5 to make some suggestions that would improve the convergence properties of a symbolic ML experiment and the very first thing it did was add an instruction for directly emitting bytes and a way to store those verbatim in the model. Perfect score achieved in record time but the result is pointless.
If you know the direction of improvement you need to head in, I think the current models could take you there. However, I do not think they are capable of deciding which direction is best, especially not when given some scalar quantity to min/max and autonomy over that process.
Isn't the harness basically where the frontier model companies can capture value and create a moat of sorts? I am also curious about building a harness for fun but would expect it to be more interesting in a scenario where I can self host an open weight model.
What motivates the people who build their own ChatGPT/Claude harness for example? And how do you keep it tuned with the rapid development of frontier models.
They're trying. I see a few main avenues:
1. Fitting the models to their specific harness, so that if you want peak model performance, you're stuck with their harness. But this only works if there aren't alternative models that are similar in capability or good enough that don't have that restriction.
2. Locking you out of the harness so that you eventually are just letting it do "stuff" with your data and system, although you don't get to see what the stuff is or why it's doing it. This is the pattern discussed in that Earendil blog post, "The session you cannot take with you." Encrypted reasoning tokens, secret agent prompts, perhaps eventually not even seeing what files are being read or what data is being sent back to their server. This way, you are also shackled to their harness because nothing is portable. But it only works if you trust them implicitly and don't have alternative models and harnesses that don't treat you this way.
3. Tying subscription pricing to the use of their harness, so that it's financially punitive to use another one. This is what Anthropic does. But again, it only works if there aren't alternative models and harnesses that work similarly well for you and don't have that restriction.
4. Marketing. Anthropic is leaning into this one heavy, from what I can tell, based on the constant ads I see for Claude Code. Can it work? People do overpay for things like vodka in fancy bottles that are chemically identical to the cheap stuff. Is Veblen-good AI a trillion dollar business, though?
I think the pressure from open source innovation in models and model tooling is going to make it a tough row to hoe. But I'm biased, as I'm actively rooting for Openthropic's demise.
The basis of the argument is that the labs are constantly pushing updates to their system prompts that are used in claude code or codex, which are exceptionally bloated and change the sand beneath your feet with every update.
By rolling your own harness, as long as you keep up with the latest advances and changes in the ecosystem, you capture a lot of the 'control' that LLM-based development feels like it strips from you.
Obvious disclaimer that I use pi.dev when I am aiming for consistency, not absolute quality. Custom harnesses on pi are what I ship, claude code is still my fallback if I need to make sure a PR is the highest absolute quality
And of course, a healthy skepticism of "the man" is essential when you're busting your ass building his unfathomably horrifying torture apparatus for him 40-50 hours a week (excluding PTO days and parental leave of course).
If anyone wants to argue that it is inefficient writing your own AI coding harness, I wouldn’t disagree. That said it is satisfying to have long coding sessions using my own tools.
The article is a valuable resource, thanks to the author.
One practical failure mode I’ve have experienced in my agentic harness tasks similar to the “weak evaluators” point: an incomplete check suite that still reports full success. That’s was worse than a weak evaluator, because it made it look correct and decisive.
After several trial and error, what helped was fail-closed on coverage i.e if the fixed checks for each operation aren’t all there, nothing ships / nothing gets sent. Otherwise you can “pass” while never running the cases that would have failed.
install or build stuff that they can then use
it changes the environment instead of the agent/harness but in a sense how separate is the agent from its environment and why do we apply this distinction re self-improvement?
animals and humans do the same thing and are great at it, without 'self-improvement' with emphasis on the 'self'