Rohan Paul@rohanpaul_ai · X本研究指出，AI代理的实际性能更多取决于围绕模型的外部控制系统（即代理框架），而非单纯的提示词。当前许多代理看似单一模型，其行为实则由规划、工具调用、记忆管理等周边代码驱动，导致长任务易因状态丢失、验证漂移等环节失败。为此，论文提出“自然语言代理框架”理念，旨在将控制流程以结构化自然语言显式表达，使其可检查、可迁移且可测试。研究发现，虽然更复杂的框架能显著改变代理行为，但并未带来稳定的性能提升，这表明框架设计是保障可靠性的关键选择，而非一种立竿见影的万能方案。
This paper shows that agent performance depends less on prompts alone and more on the harness around them.
"Agent intelligence" is becoming partly a systems problem. The problem is that many AI agents look like 1 model， but their real behavior comes from surrounding code that controls planning， tools， memory， retries， checking， and stopping.
A model may reason well in one step， but long tasks fail in messier places： state disappears， verification drifts， tools return partial evidence， and the agent forgets which intermediate artifact actually matters.
Natural-Language Agent Harnesses try to make that control layer visible.