we're continuing to see clear examples where a model's harness is a major determinant of overall performance. with the same model, running o…

we're continuing to see clear examples where a model's harness is a major determinant of overall performance. with the same model, running on same task, it's easy to observe very different scores depending on (system) prompts, tools (& their descriptions), and middleware (steering hooks). this is exactly why we built a harness profiles abstraction in Deep Agents: per-provider or per-model overrides for base system prompts, tool names + implementations, etc., so swapping models doesn't mean losing the work that made the last one good! 10–20pt jumps on tau2-bench in our own testing. currently cooking up built-in profiles for popular open weight models 🧑‍🍳 https://www.langchain.com/blog/tuning-deep-agents-different-models you've heard that models are highly trained in their harnesses, but... it appears that pi is about 7-10% better than codex with gpt-5.4 on a ProgramBench task. Same exact prompt, same environment. It's a good harness.

Source: Harrison Chase (X) | 2026-05-06