这篇来自MarkTechPost的文章深入探讨了AI开源课程中的智能体循环运行方式，对于想要了解AI工程实践的人来说，是一篇非常实用的教程。
本文探讨了在AI开源课程中，如何通过改变绑定工程来提升编码智能体的性能，并介绍了三种运行智能体循环的方式及其背后的提供商经济学。LangChain的Terminal-Bench实验表明，仅改变绑定工程，相同模型下，智能体排名从30位提升至前5。
Decoding AI’s Open-Source Course Maps Three Ways to Run an Agent Loop and the Provider Economics Behind Each
Most teams treat ‘which model’ as the important decision. The harness engineering literature keeps pointing somewhere else. In LangChain’s Terminal-Bench experiment, changing only the harness—same model throughout—moved a coding agent from roughly 30th place into the top 5. That result reframes the question. If the harness decides quality, then how you run the loop becomes […] The post Decoding AI’s Open-Source Course Maps Three Ways to Run an Agent Loop and the Provider Economics Behind Each appeared first on MarkTechPost .