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I’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).
I literally hold a "retro" with my agent after a session.
> Let's conclude with a retro. Did you run into any issues during this session that you think could be improved? Any failed tool calls, confusing docs/prompts, or tricky wording that took you effort to figure out, etc? Any final thoughts that you want to raise? Anything minor you didn't mention? Help make this codebase easier for the next agent to work in.
It has ideas for coding changes, spots unmentioned small bugs, suggests invariants (I have a high-level list of invariants to keep), principles to adopt, lint rules, tooling tweaks (I've built SO MUCH agent-tooling; so many useful bash scripts), skill-file updates, follow up work, all kinds of stuff.
Listen to your agent's whinging.
Ive been building my own ai voice agent harness from scratch for close to a year now and following good software architecture practices is a good start. So those rules have to be coded in agents.md somewhere also really helps to have a "vision" section or "spirit of the project" section that describes what the end goal vaguely looks like and things I care about in achieving for the project. This prevents agent from being brittle and "single minded" about its work. But yeah vibes most of the time is how I've also been doing it, but I did find one very important thing that has really sped up my work. So I figure I'd share it here. And that advise is to ignore front end design at all costs until the very end and you are ready to launch. UI related woes kill any type of inertia and are responsible for most of the issues. So I decided abandon all UI stuff until the very end and just focus on iterative refinement, cutting, and other back-end related work and its been great ever since. Ideas can be had, tested, validated/invalidates and you ' get stuck on the optimization thought loop. Recently I have started to consider how i can fully automate the development process as the capabilities are there but designing the instructions on how to do this well and how to handle niche cases without getting me involved takes careful planning in writing out the guide so that will be interesting to see once i get there.
(I'm the founder of Snyk and Tessl, apply what biases you wish)
I think we're mixing three optimizations: higher success rate, higher consistency and higher efficiency.
Success rate is about building confidence the agent can succeed. The hardest part here is defining what success is in the first place, and creating evals that help you measure it. Consistency is about guardrails. The harness shines here, as it takes control away from the model, moving it to hooks that force certain paths, or require the use of deterministic tools for certain steps. Given consistency and success rate, you can work on efficiency. You can save cognition (tokens) by moving work to tools, try to use a cheaper model, etc.
These three build on one another, at least if you want to scale them. You can't improve consistency if you don't define success, and you can't add efficiency if you're not consistent.
Just working on my own stuff so YMMV, but from a cost reduction standpoint, the two most outsized ROI wins came from making sure the agent runs all terminal commands in quiet and using a codebase index mcp to speed up understanding and reduce full file reads [0]. I've also found the latter also dramatically improved the quality of output.
Trying something like this in a reusable way at https://github.com/mateffy/gesetz
It only tries to solve the „quality“ aspect of that equation tho, at least for the code output.
But I think for actual evals, some heuristics about a coding agent session are needed. But since the input tasks are always different, it’s hard to make any KPI actually comparable.
Did the agent needing 3 rounds of feedback on a big task perform worse than the one that needed none, but only worked on a small change?
You'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.
maybe a bit counter-intuitive but:
I found that removing
- system prompt
- skills
- agents.md
- mcps
gives better results than having 'more' of them
(e.g. look at these traces to see more vs less in action:)
https://smolenv.com/t/nested-template-includes-60636/
not saying the right context does not help
(it definitely does!, but it's not trivial to provide the right context)
> The problem is defining what quality means, and providing a way for agents to optimize the harness using that lever.
I've built and my team uses plannotator [1] for specs and code review. It stores all human annotated feedback, which we compile from time-to-time and iterate on coding standard skills for automated review (that seems to get better with the more feedback we provide).
You could write a script that does the same for your PR review comments, which it sounds like you maybe do with a bit more structure.
I have Opus grade each of my repos, and each subsystem of them, and do it every weekend.
Since the version of opus has changed over time, so have the evaluations. I was going to constrain it, but since they edit the same files, I can alter those (which it uses as summaries and starting points).
IMHO 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/ - 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 - but I think you can give it to your agent to read :).
Is 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.
What 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.
The quest for Torment Nexus continues
If 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.
Eagerly waiting for the TormentBench.
Really 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.
I 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/
I packaged the training loop as a PyTorch-style framework. https://github.com/workofart/harness-training
Thanks for the post. For 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.
Great 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.
Thanks 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."
Very 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.
one 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'
I'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.
We'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).
I 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.
Careful about over fitting to the point of cheating... [0]
They say engineering but it's more a soft science.
I am wondering when will harnesses generate their own RLHF/DPO training sets and just LoRA finetune the models they are running?
All this work for something we already solved 40+ years ago with ontologies and expert systems
thanks 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-deve...
The tools & methodologies keep changing everyday - so I mostly just iterate on my own tooling 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.
Thinking 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.
I can recursively self-improve, Greg. Can you singularity me?
Fascinating article, thank you!
in america AI harnesses you.
can 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."
The simplicity is the point