Why the Harness Matters More Than the Model
Model-agnostic harnesses outperform single-model labs by optimizing workflows across providers
“a great model independent harness is bringing the best of all of these workflows together and actually making the models better”
A LangChain speaker argues that orchestration harnesses matter more than the underlying model, demonstrating that their workflow brings Opus 4.8 code review costs from $5-6 down to ~$3 while closing the quality gap with GPT 5.5. The claim challenges the vertical integration strategy of model labs, suggesting they face a structural disadvantage by being unable to benchmark across competing models. This reframes the AI stack debate: workflow engineering may compound faster than raw model capability gains.