i've been playing around with https://github.com/bytedance/deer-flow and they seem to figure out how to get around context sizing issues and has a more automated prompt system.

Essentially the problem with current hanrsses is they just can't find/evict context and want to ensure KV cache isn't evicted.

If they evict the old prompt and tried to keep all the proper rules, then you get token churn anyway, as what you want is an entirely new chain of events where your rules/guidance sit at the front of the model chain.

What we're getting to is there really isn't an economical way for cloud models to do it; you can do it with a local model, but at comfortable speeds, you need smaller sized.

I think a good harness via Qwen3.6-35B-A3B is possible, as the token gen is only 3B since that's the active parameters.

So it's mostly that to keep rules pinned to the top of the context will either bloat the model or it'll evict the KV cache.