Elvis Saravia 推荐了一篇 Hermes Agent 的大规模子智能体运行案例,约 1.4K 个子智能体显著减少代码行数并带来大幅成本节省。他关注到自进化技能与复利工程两项关键技术,认为这是 harness 工程的重要方向,但不确定该方案能否迁移到其他 harness。他还质疑用更少子智能体是否也能以更低成本完成同样任务。
Recommended reading. This is probably one of the bigger subagent runs showing good results.
Two things caught my attention: self-evolving skills and compounding engineering.
Both are important techniques in harness engineering.
Overall, subagents reduced LOC significantly and delivered insane cost savings.
Hermes Agent is a proper agent harness with self-improving mechanisms, but I am not sure this same approach would work in other harnesses.
What works in one harness doesn't always transfer easily to others. This is why I am excited about custom harnesses. So much optimization is left out in these general-purpose coding harnesses.
Do pay attention to these differences in your harness.
My other observation was the use of a whopping ~1.4K subagents. Yes, it reduced costs, and it probably helped with parallelizing things. But could this run have been done cheaper using fewer subagents?
This is something to explore if you are a harness engineer.
I feel like this piece also highlights the importance of the harness and the customizations needed to provide the right context to agents when they need it.
Pay attention to how your agent harness can use self-evolving skills (encoding lessons and experience) to scale agents effectively.
来源:@omarsar0 · x.com