My own findings are in line with this research:

Having a coding harness is critical but the differences between them are overstated.

Personally, I’ve replaced OpenCode with a thin wrapper around Pydantic-AI as the pythonic analogue to Pi-Agent for headless use via Hermes

They’d all do the job - I just prefer to compartmentalize for access control.

Keeping the harness’ surface area tiny had the added benefit of preserving my understanding and being able to adapt it to my preferred workflow effortlessly

> Personally, I’ve replaced OpenCode with a thin wrapper around Pydantic-AI as the pythonic analogue to Pi-Agent for headless use via Hermes

That's really interesting. I like Pydantic AI a lot and wondered why all of the harnesses seem to be written in Javascript instead of it. What do you use it for headless, though? I haven't tried Hermes or similar yet, so don't have a handle on what you do with them.

One thing I am missing to be able to move out of Claude Code, is the auto mode (and the soft_deny and hard_deny settings that can be tuned), with it's classifier checking the output.

It's the killer feature from me personally, often when wanting to troubleshoot for example things like Kubernetes workloads. LLMs are now really good at it, but we doesn't want them to like delete a pod.

Other harnesses like Codex have often on static rules, like the allow/deny of claude code, that can filter out based on regex. It's quite good already, but sometimes the model can find a way to write something that wasn't anticipated, or in a convoluted way.

After, I guess it's something that can be added in an open-source harness like Pi, and add like this new Jev model or something else equivalent