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Handbook.md shows that long policy documents do not reliably govern agents

arxiv.org

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Re: Handbook.md shows that long policy documents do not reliably govern agents

#41

Post by msejas »

Most people don't understand that 'agentic AI' is a completely synthetic, force fed capability by extensive Reinforcement Learning on synthetic domain specific 'agentic' datasets on post training.

If the LLM wasn't post-trained to adhere to specific handbook, it just won't work. If the LLM wasn't trained on an use case the lab decided was worth making a synthetic agentic dataset, it won't work as well as you want.

There's a reason the main agentic task LLMs excel at are coding tasks, it's the way of working of the creators, and they understand intimately the flow and can train for it.

I believe the true way will be able to easily fine tune models on your agentic use cases, but it would require a big company to compile a huge dataset on it's way of working and I don't think anyone wants to be the first.

In terms of long context, accurate attention retrieval from early tokens is just impossible, given the expansion of RoPE encoding for the positions, or in case of Kimi that don't use it anymore, as well as deepseek, early context is heavily compressed you lose accurate information.

If people spent more time studying about AI and how it works, they would realize that the default should be to one shot prompt your task with a big, cached system prmopt, with an user prompt that is just dynamic data, specified to the cheapest model that can do the job.

Unless you really can't do this given your problem, you should try to make a graph of well defined, step by step oneshot prompts, and THEN if your problem still can't be solved with that, then you start leveraging agents.

Despite this giving better results, and being more cost efficient, is evidently too much work then just letting the AI do all the work.

Re: Handbook.md shows that long policy documents do not reliably govern agents

#42

Post by agotterer »

mcdeltat wrote: post #3

Yeah checks out with my anecdotal experience with Claude. It is pretty great at following instructions - for about 10 minutes, after which it seems to ignore things I told it before. I have quite explicit and strong instructions (e.g. don't write massive comments, use existing functionality, etc.) in CLAUDE.md files which seem to get bypassed surprisingly quickly when doing real tasks. Yet if I tell it these things i…

I’ve had a lot of success using the root Claude.md for a handful of high level application wide rules and directions (I keep it pretty small), module specific claude.md in subfolders alongside the code with more specific rules and direction, and a custom rules backed /code-review skill that enforces it all and catches anything that was missed during implementation.

Re: Handbook.md shows that long policy documents do not reliably govern agents

#43

Post by drob518 »

msejas wrote: post #41

Most people don't understand that 'agentic AI' is a completely synthetic, force fed capability by extensive Reinforcement Learning on synthetic domain specific 'agentic' datasets on post training. If the LLM wasn't post-trained to adhere to specific handbook, it just won't work. If the LLM wasn't trained on an use case the lab decided was worth making a synthetic agentic dataset, it won't work as well as you want. Th…

What do you mean by a graph of one shot prompts?

Re: Handbook.md shows that long policy documents do not reliably govern agents

#44

Post by Muromec »

TeMPOraL wrote: post #35
Muromec wrote: post #24

Earlier quoted context omitted.

Don't use the default harness, write your own instead.

Any way to do that AND use subscription instead of per-token pricing from SOTA providers?

If the harness runs on your localhost, but the inference doesn't, it usually means it's calling some API. Whether you want to break TOS of your provider like that or simply buy tokens from our friends from UTC+8 timezone is an open question.

Re: Handbook.md shows that long policy documents do not reliably govern agents

#46

Post by cyanydeez »

mcdeltat wrote: post #11
cyanydeez wrote: post #5

Earlier quoted context omitted.

I believe the correct static instructions are about getting it at the right starting point for whatever class of projects you're working on; not as a continued referencable or "HOWTO" of what it's doing. They're all just "grooming" the LLM for future instructions. The coding harness is what's getting it to continually align to your current instructions. This is very obvious with local models.

Ok so what is the correct way to tell it "I don't care what is happening, you must uphold these rules at all times"? If it's not any configuration of .md files?

in the plugins I'm using, it's basically _always_ adding information to the context. You're not going to do it manually; you can't go back in the context and add it because that'll break the cache. The way your programming harness works is by constantly reminding the LLM of the tools available.

A good programming harness is basically a stack. A good stack keeps building each layer. You _cannot_ pull things off the bottom of the stack because that's an expensive cache hit; but you can pull things off the top. So what your harness should be doing: onto each request _then_, when you get to the next request or result, pulling that out if there's some change.

So you can see it's the cache that's either exponentially growing or having to cache bust to keep it fresh.

Re: Handbook.md shows that long policy documents do not reliably govern agents

#47

Post by DanHulton »

simpaticoder wrote: post #13
DiabloD3 wrote: post #6

This is a problem with long context models. To put it as simple and as bluntly as possible: just because they claim you can use 1M tokens in your context doesn't mean its true and you should do that. Due to extreme quantization of models and the context's KV cache, and also just really shitty samplers provided to the user (hell, most are just getting rid of sampler knobs altogether), this problem will absolutely cont…

>Want it to go away, almost like magic? Local inference. Ah yes, magic that costs the same as a new car.

Not necessarily!

If you have a semi-recent MacBook with even 32GB, you can run 20GB models that are pretty damn smart, with room to spare for the rest of your toolchain.

If you’re reasonably connected to the code you’re writing and prompting the AI at the level of the code, not the level of the feature, you can get some fantastic results.

Sure, it’s not the completely automated dreamland that’s been sold, but it’s still a speed-up on par from going from assembler to a higher-level language, which is still immense.

And for effectively free, if you have a machine that would otherwise have been considered “developer-grade” for a lot of tasks anyway.

Re: Handbook.md shows that long policy documents do not reliably govern agents

#48

Post by schmuhblaster »

For my own (rather idiosyncratic) harness I've been experimenting [0] with "compiling" long markdown specifications into small executable logic programs. It's too early to tell for sure, but I believe that this approach does have its merits when you want some guarantees about how your agents behave for longer tasks.

[0] https://github.com/deepclause/deepclause-sdk

Re: Handbook.md shows that long policy documents do not reliably govern agents

#49

Post by eshack94 »

DiabloD3 wrote: post #6

This is a problem with long context models. To put it as simple and as bluntly as possible: just because they claim you can use 1M tokens in your context doesn't mean its true and you should do that. Due to extreme quantization of models and the context's KV cache, and also just really shitty samplers provided to the user (hell, most are just getting rid of sampler knobs altogether), this problem will absolutely cont…

I've noticed this trend of the sampler knobs being removed. Can you explain why this might be the case?

Re: Handbook.md shows that long policy documents do not reliably govern agents

#50

Post by cyanydeez »

spIrr wrote: post #8
cyanydeez wrote: post #5

Earlier quoted context omitted.

I believe the correct static instructions are about getting it at the right starting point for whatever class of projects you're working on; not as a continued referencable or "HOWTO" of what it's doing. They're all just "grooming" the LLM for future instructions. The coding harness is what's getting it to continually align to your current instructions. This is very obvious with local models.

As a hobbyist, I find it difficult to figure out how to make Claude stick with some repeating things I want it to do after every major action, like re-evaluate the completeness of tests, update the documentation, etc. And CLAUDE.md/AGENTS.md definitely did NOT help there, sadly.

What I'm currently doing in a large refactor, is I created a super-run script; the super run script is devided into super-dev (Setup dev), super-test (run all tests), super-build (build artificats), super-e2e (test all artifacts), super-deploy (deploy finished).

Each super's sub functions should _fail hard_, and each script should be highly detailed; of course I'm not doing it myself, but in small increments of directed work, it can build up the necessary harness.

What I get is a CI that just starts with "run super-run.sh" and that gives it context, then each sub script provides context depending on if it succeeds or fails. If it fails, the agent is provided what it needs.

It's basically, you have to design the products of the AI to give itself the context. Another technique I'm testing out is a parallel set of files like .js, .test.js, .md which get pulled up if the Agent is looking for a file.

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