Nvidia research: agent harness matters more than model quality

Nvidia research shows that AI agents can perform reliably on tasks, without going off track, primarily through fine-tuning of the surrounding harness rather than improvements to the underlying model itself. The finding held even when the base model used wasn't particularly strong at the task.

Why it matters: This supports a broader shift in how AI capability gains are being framed: less about raw model scale, and more about the scaffolding, tools, and fine-tuned control logic wrapped around a model — a trend also reflected in industry commentary about harnesses being absorbed into or replacing model-centric approaches.