When i wrote my own harness, i didn't rely on what other agents do, but rather sat down and considered how i can solve the issues that i have when using other agents. And tbh, for alot of problems there are rather "simple" fixes - each comes with a cost tho. For me it was accepting to run more inference than "standard" agents but in return solving alot of issues those agents have. And to me having a full autonomous agent that i can task and just come back hours later to a solution is much more worth than just an agent that i need to "prompt jokey" the whole day.
I think its important that people don't just look at whats implemented rn, but rather just try out new things , not just "adjust" the existing ones. Thats how we got better progress, not by trying to build the 1000s variant of context compression but rather reconsider how can context be build to not have this issue (simple example).
Harness optimization is essential. Doesn’t matter how smart the model is if the tools you give it are ineffective, or the task is under defined. It needs invent the solution from first principles every time.
Instead of reading AI-slop explanations full of creaky metaphors, I'd advise you to just build your own basic coding harness from scratch. You will learn so much:
1) This old but still excellent blog post by Thorsten Ball is a good place to start (old in LLM years... written 18 months ago!): https://ampcode.com/notes/how-to-build-an-agent . It is written by a human, and gets across the core message really well. If you don't enjoy reading Go then just use an LLM to translate the samples into Python code or whatever.
2) Instead of relying on an api key like in the blog post in 1), you might want to use a local model that can do tool calls like Qwen 27B.
3) Use a frontier LLM for self-study and to help you write the harness boilerplace if you want, but ask it to force you to write the interesting bits by hand (e.g. the main loop structure, the tool call functions). Yes, coding by hand is still useful as an educational mechanism to force you to internalize some tricky concept. Be disciplined and don't slip into letting the LLM write the core logic for you.
4) Amaze your friends and colleagues with your newfound understanding of how agents actually work under the hood
5) Go even deeper and get into Inference engineering, LLM architecture internals, etc.
6) Profit. Perhaps? (Or at least enjoy the learning journey.)
The LLM is the oven, the harness is the kitchen. You prep all your ingredients and put it into the oven (flour, water, salt, sugar). It transforms those inputs into a finished product (cake, loaf of bread).
Nah, that's a single event ("bake"). Interaction of a harness and an LLM could potentially be a continuous process.
I don't think we've scratched the surface of how the harness can fold, spindle and mutilate the LLM. It just seems unlikely to me that we happened to stumble on the best possible interface a couple of years out of the gate.
If LLMs were brains, it would be someone with severe amnesia who you had to tell them what an arm and a leg were every time. Its stored vectors of words or partial words and computations of math that run to predict the next words/partials to use to form text output. It will nearly always find the real sets of words that are load bearing to put into its output (seriously how many times do real and load bearing appear in the models, can we remove some of that).
What's interesting is most of the progression we've seen this last year in AI agent clients are very basic computer science concepts: loops, threads, arrays (of JSON) and memory management (aka context) with varying combinations of those put together and people in computing are writing about it like its some huge break through. I would love to hear a game developers perspective on comparing AI clients (not the server side LLMs/vectors/maths) with games - they seem to have very similar computing problems.
I don’t understand why this article makes you ask that, can you elaborate? The simple answer to why we didn’t have LLMs in 2005 is that nobody had the requisite ideas that make them work effectively (independent of any agentic harnesses and Schmidhuber notwithstanding) and there wasn’t sufficient clarity in the direction to scale for anyone to build anything at scale (also a requisite for them working well).
Surely the only problem isn’t the knowledge of how to train and the confidence it would work. Wouldn’t it also have required a 1 or 2 order of magnitude increase in computing power over 2005-era supercomputers to be able to train a modern LLM in a reasonable time (“reasonable” meaning “finish training before GPT-3 comes out in 15 years”)?
I think it’s plausible that attention could have been invented earlier and GPT demonstrated at smaller scale on earlier hardware. Then hardware timelines might have accelerated (or alternative approaches explored) and we’d have gotten to where we are earlier.
Fair on the prose. I explained how it was written in my top-level comment. If any of the ideas themselves seem wrong to you, I'd like to hear which ones.
I think its important that people don't just look at whats implemented rn, but rather just try out new things , not just "adjust" the existing ones. Thats how we got better progress, not by trying to build the 1000s variant of context compression but rather reconsider how can context be build to not have this issue (simple example).
We did a harness optimization on Jev. Raw it was no where near as good as deekseek, but optimized it was significantly better and a tiny fraction of the cost. https://kiln.tech/blog/auto_optimizing_jev_with_autoresearch
1) This old but still excellent blog post by Thorsten Ball is a good place to start (old in LLM years... written 18 months ago!): https://ampcode.com/notes/how-to-build-an-agent . It is written by a human, and gets across the core message really well. If you don't enjoy reading Go then just use an LLM to translate the samples into Python code or whatever.
2) Instead of relying on an api key like in the blog post in 1), you might want to use a local model that can do tool calls like Qwen 27B.
3) Use a frontier LLM for self-study and to help you write the harness boilerplace if you want, but ask it to force you to write the interesting bits by hand (e.g. the main loop structure, the tool call functions). Yes, coding by hand is still useful as an educational mechanism to force you to internalize some tricky concept. Be disciplined and don't slip into letting the LLM write the core logic for you.
4) Amaze your friends and colleagues with your newfound understanding of how agents actually work under the hood
5) Go even deeper and get into Inference engineering, LLM architecture internals, etc.
6) Profit. Perhaps? (Or at least enjoy the learning journey.)
I don't think we've scratched the surface of how the harness can fold, spindle and mutilate the LLM. It just seems unlikely to me that we happened to stumble on the best possible interface a couple of years out of the gate.
What's interesting is most of the progression we've seen this last year in AI agent clients are very basic computer science concepts: loops, threads, arrays (of JSON) and memory management (aka context) with varying combinations of those put together and people in computing are writing about it like its some huge break through. I would love to hear a game developers perspective on comparing AI clients (not the server side LLMs/vectors/maths) with games - they seem to have very similar computing problems.
https://hindex.rensberger.xyz/
Why this spate of pretending, against all evidence, that LLMs are alive, or even remotely structured like biological brains?
Why do we have so many people pretending, or disingenuously claiming, that they're alive, have thoughts, feelings, etc?
And why, oh why, do we have to read yet more LLM propaganda written by LLMs?
You could view LLMs as a super-charged mutation function inside a genetic programming harness.
There's probably something there, though im not sure what. I'm thinking about LLMs token-filling and the 4 amino bases
On the other hand, Google was openly collecting data with a view towards building AI.
You can make the argument that some eng aren't good at writing, to which I say — it's worthwhile to improve writing skills.
No? Where?