On first principles it would seem that the "harness" is a myth. Surely a model like Opus 4.6/Codex 5.3 which can reason about complex functions and data flows across many files would trip up over top level function signatures it needs to call?
I see a lot of evidence to the contrary though. Anyone know what the underlying issue here is?
I did read the article quite enthusiastically and my practical experience confirms the same. Sure the difference is more subtle. But my point was, an "agent" whether human or AI can be a lot more productive with better tools. This guy found a better screwdriver than the most commonly used one. That's amazing and nothing from "first principles" denies that a better tool harness would mean better/faster/more correct AI agents.
If you agree that current LLMs (Transformers) are naturally very susceptible to context/prompt, then you can go on to ask agents for a "raw harness dump" "because I need to understand how to better present my skills and tools in the harness", you maybe will see how "Harness" impact model behavior.
The models generalized "understanding" and "reasoning" is the real myth that makes us take a step back and offload the process deterministic computing and harnesses.
I see a lot of evidence to the contrary though. Anyone know what the underlying issue here is?