Nvidia research argues agent performance depends heavily on orchestration and fine-tuning, not just the base model

TechCrunch reports on Nvidia research suggesting that AI agents can become more capable and stable through improvements to the surrounding harness, including fine-tuning and task orchestration, even when the underlying model is not best-in-class for the task. The piece frames the result as evidence that agent system design may matter as much as, or more than, incremental gains in the core model.