Harness Engineering 101: The LLM is the brain, the harness is the body (github.com)

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

    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).

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'm currently running a setup where Claude lives in a container with zero auth token and I have a secondary gateway container that intercepts all requests that require a token and injects them into the request. For things like AWS request it intercepts the request and signs it. There's also a firewall to limit the requests it can make to a few known hosts.

I'm guessing and doing a lot of trial and errors and I want to know if there's any best practice. Googling only seems to return results from 2025 when people were just getting started with LLMs.

I mean, it can be that way. But it does't have to be. The Harness can be as much the brain as the LLM. You have nearly unlimited flexibility in what you do in the harness, and you can add as much of the overall "intelligence" of the system as you want in the harness.

To illustrate, you can:

1. Use multiple "brains" / LLM's, and do routing between them using a specialized model (a Jev like model, or something of your own design)

2. Use planning tools like pyperplan, jupyddl, pddl4j, etc.

3. Implement your own "memory" system using... pretty much anything you want. And I would argue that a lot of the apparent "intelligence" of a system involves how well memory works

4. Call classical machine learning models to solve specialized problems

5. Use reinforcement learning to improve performance in specific areas

6. Implement a blackboard for multiple knowledge sources to collaborate.

7. Use inference engines using forward/backward chaining, RETE, etc.

8. Use your own hand-rolled abductive inference strategy

9. Implement your own Bayesian reasoning techniques

10. Use custom causal inference code

... and so on.

Don't get me wrong. I'm bullish on what LLM's can do, but I posit that the overall expressed intelligence of a system can involve a lot more than just the LLM. In fact, I'd actually make the slightly stronger claim that, depending on what you're trying to do, an LLM might not even be required at all.

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?