Elvis Saravia 点评 NousResearch 用 Hermes Agent 与 1393 个子代理重构百万行 Python

elvis · @omarsar0 · X·2026-09-17 07:15·2小时前
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NousResearch 于 9 月 2 日让 Hermes Agent 清理约一百万行 Python,动用 1393 个子代理、耗时十九小时,代码库缩小 34.4%,节省近 200 万美元工程时间。Elvis Saravia 转发并点评这是规模较大的子代理运行案例,关注自进化技能与工程复利,指出该方法未必适用于其他 harness,并质疑用更少子代理能否更省地完成。

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Elvis Saravia 点评 NousResearch 用 Hermes Agent 与 1393 个子代理重构百万行 Python

2026-09-17 07:15· 2小时前
AI 导读

NousResearch 于 9 月 2 日让 Hermes Agent 清理约一百万行 Python,动用 1393 个子代理、耗时十九小时,代码库缩小 34.4%,节省近 200 万美元工程时间。Elvis Saravia 转发并点评这是规模较大的子代理运行案例,关注自进化技能与工程复利,指出该方法未必适用于其他 harness,并质疑用更少子代理能否更省地完成。

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.

Nous ResearchNew blog post: We had a million lines of Python to clean up. On September 2nd @Teknium asked Hermes Agent to do it. 1,393 subagents and nineteen hours later, th...