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Harness engineering for self-improvement
- manojbajaj95 3mo agoI've been working with self improvement harness a little bit and one thing i've come to conclusion is harness task fit. The learning can be significantly improved if we understand the behaviour of task and how it should be learned. I'm pretty sure a general solution will definitely exist which will do fine, but we are yet to see one.
- epolanski 3mo agoI haven't understood a bit. Can you make an example?
- djsavvy 3mo agoAgree with the sibling comment that an example would be useful. I'm also curious what kinds of tasks you have in mind. Theoretically almost every knowledge work task boils down to the file editing that agent harnesses already are built around (which probably explains the second half of your comment).
- sulot3212 3mo ago[flagged]
- amelius 3mo agoThey say engineering but it's more a soft science.
- adoltech 3mo ago[dead]
- Kinrany 3mo agoThe quest for Torment Nexus continues
- Drakim 3mo agoIf we don't create the Torment Nexus first, somebody else who is much less responsible is gonna create the Torment Nexus before us. It's outright irresponsible to not take the lead, we might have to even give up on all safety concerns to make sure we make it to Torment Nexus IPO first.
- fineIllregister 3mo agoWe cannot allow a Torment Nexus gap!
- K0balt 3mo agoSince we all know that at this point, someone is definitely going to build the torment nexus, we all may as well get involved and help, so that at least we can exert some limited degree of control over the eventual character of the torment nexus. At least that way maybe we can make it a little less terrible. At any rate, it can’t make it worse than it would have been if we -didn’t- help build it!
- cyanydeez 3mo agowhats the other option, touch grass? not likely!
- tdeck 3mo agoIf you're going to get involved in a torment nexus project, at least do yourself a favor and research the different comp packages being offered and choose the best one. 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).
- velcrovan 3mo agohttps://fundthebasilisk.com https://fundthebasilisk.com
- deleted 3mo ago[deleted]
- tosh 3mo agoone form of very effective self-improvement that coding agents do all the time: 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'
- sigbottle 3mo agoBlame Descartes? I don't know. Extended theories of cognition all get shot down, even analytic philosophers who are accused of being more "continental" still primarily hold mind-body dualisms
- devilfileprong 3mo ago[dead]
- devilfileprong 3mo ago[flagged]
- hn5xz7plcj 3mo agoThe simplicity is the point
- gopalraja 3mo agoReally good points Lillian. Agreed on keeping the evaluator outside the loop that evolves the harness. 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.
- cahaya 3mo agoThanks for the post. For https://Document.bot https://Document.bot (Kinda Cursor IDE for knowledge workers), im already trying to improve the harness (besides spotting bugs) using hillclimb experiments. More and more i'm using a AI harness engineering skill in Codex to further improve the app. This blog post helped me to improve the skill a bit.
- robbru 3mo agoVery interesting, will give it a demo.
- mark_l_watson 3mo agoGreat article! I am currently writing my second harness (first was in Emacs Lisp, using Emacs as UI; second is a command line coding agent written in Common Lisp). 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.
- lmf4lol 3mo agoThe dream of the AI lisp machine ! Did you build in the functionality that the agent can on the fly rewrite its own (harness)-code?
- mark_l_watson 3mo agoNo, but a good idea. I want something simple, practical, understandable. I think I will write a short book on this general topic.
- sroerick 3mo agoI stuck a lisp interpreter inside a web server and its a pretty cool pattern. I do have a basic harness but I'm still kind of just using the REPL from an external agent over REST, which seems to work just as well without having to go through the brain damage of building a harness
- sroerick 3mo agoI'm curious to learn more about your experiences here, what you've learned, if you've enjoyed using the REPL with agents
- bob1029 3mo agoI think the biggest problem with RSI is the tendency of models to try and find "clever" solutions when presented with very hard problems (aka cheat). 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.
- keremturhan 3mo ago[flagged]
- kriro 3mo agoVery enjoyable article. 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.
- sbysb 3mo agoThere is a really good video by the author of pi.dev (which I have used to build some of my own harnesses): https://www.youtube.com/watch?v=RjfbvDXpFls https://www.youtube.com/watch?v=RjfbvDXpFls 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
- intrasight 3mo agoMy own opinion is that the "harness companies" (Anthropic, OpenAI, Cursor etc.) are fucked long-term since customers will tire of that quicksand. As the models improve, we will tire of that harness crap.
- anon373839 3mo ago> Isn't the harness basically where the frontier model companies can capture value and create a moat of sorts? 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.
- xander_north 3mo agoFascinating article, thank you!
- zby 3mo agoIMHO training weights has peaked and now it is time for a training paradigm for prompts and code. We don't have the gradient descent here - but I think it can be more sample efficient because causal theories can be better than just correlations. I am working on a unified theory in https://zby.github.io/commonplace/ 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-improvement/ https://zby.github.io/commonplace/articles/reflective-self-i... - (this is not stable yet - but I think you can give it to your agent to read :).
- ianm218 3mo agoIs there any reason to think that training weights has peaked rather than is accelerating? It feels like now they are increasingly able to pick some low hanging fruit by using the models in order to improve themselves and test optimizations.
- zby 3mo agoto be frank - mostly because they are now good enough to unlock the other learning paradigms
- victorbjorklund 3mo agoWhat date was the peak? If it is today it’s not something you can know so I assume you think the peak was many months ago.
- zby 3mo agoI think it is now or close to now - and not because there is no more to be gained - but rather because it is now clear that you can gain more with the right agent environment. But benchmark saturation is also something to account for.
- erwincoumans 3mo agoThanks for the really nice in-depth post! Hoping for a future one about: "Much recent work on auto-research, self-improving agents, and evolutionary program search can be organized around this question. Other work on model self-play, synthetic data, test-time training and a broader theme of continual learning also matches the RSI vision (e.g. Yuan et al. 2024, Chen et al. 2024), Zhao et al. 2025, Choi et al. 2026)) but they will not be the focus of this post."
- BehanPrW 3mo agocan you elaborate this part please? "The workflow graph also emphasizes the model analyzing its own trajectories and failure cases and then iterating on its progress through an “agent runtime” rather than a static prompt template."
- bisonbear 3mo agoI’ve been thinking about how we practically implement this at an organizational layer for large codebases. There’s clearly alpha to be had in optimizing AGENTS.md / skills / tools / … to improve performance, quality, and cost efficiency of an agent. The problem is defining what quality means, and providing a way for agents to optimize the harness using that lever. The first step I see towards this is building a generic, reliable, and accurate *fitness function* for codebases - turning PRs into gradeable tasks that an agent can solve, and improve on. I’m pretty curious how others are optimizing the coding agent harness now, as this has been a huge pain point for my company (we’re pretty much relying solely on vibes).
- clamshelldev 3mo ago[flagged]
- sulam 3mo agoYou've just described what the AI industry refers to as "evals", a collection of which forms a particular benchmark. I definitely encourage you to define your own evals, because the public benchmarks are often either saturated (largely solved and only going to see small improvements going forward) or seemingly not predictive of real world performance. I could also go further and speculate that they are in the training data, although credible benchmarks avoid this, I'm just not sure how successful they are.
- bisonbear 3mo agoThanks for the comment - I’ve actually been working on something to generate evals from private repos, as I 100% agree that public benchmarks are either contaminated (as OpenAI found with SWE-bench Verified), not high quality (as OpenAI found with SWE-bench Pro), or in the case of “good” benchmarks (Cognition’s Frontier Code comes to mind), non-representative of a specific repo’s work. Sharing some challenges / learnings from the journey so far: * Tests alone are non-representative: an agent can pass tests but write the code in a way that’s subjectively worse than another test-passing result * However, tests are still important and the best form of deterministic evaluation we have * Creating executable environments to run the tests is challenging, especially when considering that this should be doable for arbitrary repos * Selecting tasks that discriminate is an art - they need to be challenging, but not too challenging, represent the variety of work that’s done in the repo, and contain tests that failed prior to the change * LLMs / agents are very powerful at judging code - but doing so in a way that is calibrated, consistent, and representative of codebase standards requires careful rubric creation and grading * I’ve shifted towards using agents (from “static” LLM calls) to generate the rubrics, and to later grade the rubrics. This is powerful but even more non-deterministic
- knighthacker 3mo ago[flagged]
- sim04ful 3mo agoAll this work for something we already solved 40+ years ago with ontologies and expert systems
- kosolam 3mo agoWhat do you mean please give more information.
- datadrivenangel 3mo agoCareful about over fitting to the point of cheating... [0] 0 - https://arxiv.org/abs/2604.11806 https://arxiv.org/abs/2604.11806
- kimjune01 3mo agoThinking that harnesses and agents can improve without human involvement, even if it works, will yield a much lower growth rate than if a human gets involved in the loop.
- gnarbarian 3mo agoin america AI harnesses you.
- unjuno 3mo ago[dead]
- scosman 3mo agoWe've using auto-research for harnesses and it's surprisingly powerful. It's amazing how many problems are easy to spot and fix from traces. Few key things were required to get it working well: 1) let it read a heap of prod traces to spot real issues. 2) let it write it's own tools (example "loading context" goes from 20k tokens across 15 tool calls to 800 tokens and 1 call to session_context tool). 3) you need evals and val/test splits, it will reward hack. 4) you need proper tooling (synthetic users, synthetic tools) for it to be able to crunch for 12 hours and produce something. 5) you need the optimization target to be a reasonable size: not your 1M line codebase, but a lighter agent harness (pi harness, skill only, Kiln harness).
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- storus 3mo agoI am wondering when will harnesses generate their own RLHF/DPO training sets and just LoRA finetune the models they are running?
- an0malous 3mo agoI can recursively self-improve, Greg. Can you singularity me?
- megadragon9 3mo agoI did something similar. It started off as a "self-improving agent" project, inspired by autoresearch, then later on I reframed it as "harness training" (discrete program search) borrowing the mental model from ML training. I "trained" the harness on a subset of Terminal-Bench 2.0 tasks while keeping the LLM (local Qwen3.6-35B A3B) frozen. Making LLM inference and the task environments fully deterministic was necessary for clean credit assignment. I learned this the hard way after spending the initial 1 month on experiment noise. My final results showed that on the full 89-task Terminal-Bench 2.0 suite, the trained harness matched or beat the official Terminus 2 harness for four LLMs that it never collaborated with during training (e.g. GPT-OSS-120B score increased from 18.7% to 36%, while using 55% fewer input tokens per solve). A harness trained only on SWE-bench improved Terminal-Bench scores too. Here's the write-up: https://www.henrypan.com/blog/2026-07-18-harness-training/ https://www.henrypan.com/blog/2026-07-18-harness-training/ I packaged the training loop as a PyTorch-style framework. https://github.com/workofart/harness-training https://github.com/workofart/harness-training
- aaronzhang42 3mo ago[dead]
- NelsonGr 3mo ago[dead]
- FacelessAICoder 3mo agothanks for posting the question - this space certainly keeps evolving I've been trying to tackle various aspects of traceability and validation at scale... embedding the self improvement & validation in various iterations of Ralph Loops.... here's a deeper write up: https://dataspheres.ai/pages/dataspheres-ai/spec-driven-development https://dataspheres.ai/pages/dataspheres-ai/spec-driven-deve... The tools & methodologies keep changing everyday - so I mostly just iterate on my own tooling https://github.com/geekdreamzz/ari-dai-skills https://github.com/geekdreamzz/ari-dai-skills so I can keep learning and iterating.... it's on my todo list to create some videos demoing it... At a high-level - I find state management is really hard at scale in addition to managing all the context for specific situations. What I do is create a graph that tracks your original.... prompts <> specs <> tasks <> ai-generated code/content.... so it's never a question on why/what since it's all instrumented into a chain. The tasks have clear validation criteria and gates between statuses to adversarially challenge the status updates and validation. Where appropriate I have playwright take screenshots and review it as part of the chain. All of this gets tracked into a dashboard so it can just keep running while you're on the go.. I'm talking high-level and a bit all over the place because there is a lot of moving parts in this space. Would love to connect more on how you and others are tackling this. I'm using Claude Code to run the ari-dai-skills repo I linked above for all my initiatives now. I'm spending a lot of $$$ on Claude and more & more I'm itching to just invest in local compute and run more local LLM tasks autonomously because we're all iterating so much. I've been able to build a lot though but I think the open source models and the local inference is getting better.... so a new wave of tooling is on the horizon to make traceability and validation better so keep hacking ... best of luck... these harnesses are like a casino & sometimes we do win big ...I think we're a long way out from real consistency since there's just so many perspectives a harness facilitates.