Every role but the verifier is the same
underlying model, called in a fresh context
and coordinating through a shared workspace on disk:
<ws>/task.md - the problem statement
<ws>/plan.md - the manager’s overarching plan
<ws>/tasks.json - a debug mirror of the task list
<ws>/notes.md - curated ideas / findings / partial proofs
<ws>/solution.py - the current solution
My use of Qwen 3.8 27B (which is the only model i use almost since it came out) is really doing something very similar to the above where first a task is described, a `PLAN.md` file is made with the (current) plan and tasks are either in the plan file or in a separate issue tracker the LLM has access to via a CLI tool, a `STATUS.md` that tracks the current status and a `GOTCHAS.md` file for writing down various notes/tidbits/gotchas (TBH this is largely because Qwen 3.8 27B does want to note down stuff anyway so it mainly acts as an attractor for not writing its notes down in the other two places :-P).
There isn't any real harness driving this, i just give it a task, tell it how it'll be handled and off it goes. I guess i'm playing the "role" of the harness there as i tell it to not go and do everything in one go :-P but i never bother with fresh contexts, Pi's "rolling" approach to compaction (i.e. summarizing the older messages to make space while keeping the recent messages as-is) make it almost a non-issue.
I have considered making it a planning tool, though as some tool it can use via the CLI (it is running in a container), not a harness - but at the same time it feels like it can do the same thing with the `PLAN.md`/`STATUS.md`/`GOTCHAS.md` triplet. The only thing the tool would help with is to provide a UI for me to check what it's currently doing, but even then i usually just stop it and ask it directly :-P then tell it to continue.
Perhaps at some point i'll make some custom benchmark with stuff that are vaguely similar to what i work on and try a bunch of different approaches. It is something i want to do anyway to see how and if Qwen4 27B (whenever that comes it) is an improvement over 3.8.
The tool that implements this is the harness. An LLM by itself cannot "go off" and "do things" - it can only complete text blocks. A harness bridges this to real world actions or just a chat box interface.
However there is a bug somewhere between my instructions and the model - i think it is the jinja template, though i'm not sure as i haven't actually looked too deep into it - that in some cases make my last message be sent repeatedly to the model after each tool call, making the model think that i'm getting impatient or something :-P. When i notice it, i tell the model that this is some harness bug and it can ignore the repeated messages, which it does.
It seems to happen when i stop it while it is thinking after a tool call but i'm not certain. I try to stop it while some tool call is in process so it cancels the call as that seems to avoid the issue. Restarting llama-server also seems to make the issue go away so it might be a llama.cpp bug, but TBH i haven't found a clear repro case aside from it happening when i interrupt it while it is thinking. I think it is more common when the context is close to full, or at least i mainly remember it happening when i've been using a session for some time (i.e. not in fresh sessions, which does make reproducing it a bit harder).
In any case, the model itself seems to deal with all the above fine (and accepts the bug explanation for repeated messages), it is the rest of the stack that can show an issue with interrupting it (but even that is relatively rare).
It's really interesting to see the progress in the 27-30B range, they have gotten quite good but still make noticeably more hallucinations or bad assumptions (ime).
IMHO you need at least 500B weights before the confabulation problem is really under control. i.e. DeepSeek 4.1 or GLM.
I admit some days I'm just lazy and throw a whole Kimi or QwenMax at it.
It used to be my go-to "cheap model". I'm glad I have 4.1 now for that.
It's definitely much better at bigger sizes, but I've still seen it at the frontier when coworkers ask me about changes their Ai made to code I originally wrote. (a good habit that organically emerged on the dev team)