Cursor 为训练下一代 Composer,构建了一个始终运行的递归智能体系统。主智能体在远程机器上通过 SSH 管理数百个子智能体,将状态收集到磁盘“收件箱”,循环检查集群健康并保持任务运行,通过 Slack 向团队报告问题。主智能体具备多种技能用于运行和监控 ML 实验。研究人员可并行运行数千个实验,大幅提升效率。对于可验证的问题,投入更多 tokens 能更快解决。
http://x.com/i/article/2065439304785039360
Building recursive agent systems
At Cursor, we run thousands of agents to help us train the next version of Composer.
We give them research tasks, and if they aren't succeeding or run into issues, they DM us on Slack or page us via PagerDuty.
Scaling training for Composer
We've built an org chart of agents that work together.
As we've scaled training for Composer, we've wanted to run thousands more experiments. This was possible before, but it was slow and hard to keep track of every experiment's status. To speed things up and parallelize work, we built an always-running agent system (yes, it's a loop).