Nous Research 用 1,393 个子代理重构百万行 Python 代码，节省近 200 万美元

Recommended reading. This is probably one of the bigger subagent runs showing good results. Two th...

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 Research @NousResearch New 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, the codebase was 34.4% smaller, saving us nearly $2m in engineering hours. nousresearch.com/refactoring-he… 🔗 View Quoted Tweet 💬 6 🔄 2 ❤️ 16 👀 2960 📊 8 ⚡