Nvidia has published research indicating that the effectiveness of an AI agent may depend more on its surrounding harness than on the raw capability of the underlying model. According to the description, fine-tuning can enable agents to perform well and behave reliably, even when the base model is not particularly strong at the given task.
Why it matters
The finding shifts attention from model selection alone toward the engineering scaffolding that guides an agent. If fine-tuning and the harness can compensate for a less capable model, teams may be able to achieve dependable agent behavior without always relying on the most powerful models.
Who should care
Developers and organizations building AI agents are the most directly affected, as the research points to fine-tuning and harness design as levers for improving agent reliability.