My reading of that isn't that the harness matters so much as the overall platform environment that agents operate in and the approach taken by the team.
> Before Blitzy starts any work on code generation, the platform launches collaborative agents to deeply analyze the repository – mapping dependencies, understanding conventions, and capturing domain logic. This documentation process can take hours or days. When prompted to add a feature, refactor code or fix bugs, Blitzy replies with a highly detailed technical specification.
The same approach could be taken with any harness with a skill to perform this step first before starting work.
What exactly are you pointing out? I read the link and the linked thread and it's not clear what position is being presented.
I don't see evidence that the harness -- rather than the approach to information indexing and agent tooling -- makes much of a difference.
You can make a case "this harness bakes X in" (or in the case of pi "this harness bakes nothing in; you choose your own adventure"), but at the end of the day, skills are just markdown files and CLIs and shell scripts can be used by any harness; they are portable. CC allows override of the system prompt[0] and I would guess most harnesses have similar facilities. I don't see how the harness is going to be the bigger impact versus the configured tooling (skills, scripts, plugins).
The extraordinary claim here is that if I configured pi and CC, Codex, etc. with the same system prompt, same tools, same skills, that pi would outperform CC, Codex. That's what it means to say the harness matters. That just doesn't seem right; rather its the configuration of tools, skills, and default prompt that matters.
My point is pi-coding-agent [1] is a very well designed and implemented open source project that we all can learn from as software engineers. His blog post about his decision making [2] is also very well written.
I should've given original links instead of noisy HN threads.
Here is a session of pi analyzing coding-agent package itself.
https://ontouchstart.github.io/.pi/agent/sessions/--pi-mono-...
It was created via a non-interactive CLI command in a docker container connected to a local llama.cpp server with a very limited model. $0 token cost.