用 Jev 驱动 agent 主循环的失败实测
作者周末尝试让 Jev 而非 LLM 主导 agent 决策循环，在财务建模基准上只做到约 20%，远低于 LLM agent 的 80%，结论是 Jev 更适合有界任务和作为经典循环中的工具。
sharing my failed attempt at building a jev-native agent so i spent the whole weekend trying to see if i can replace the classic llm-driven agent loop to be jev-driven as in jev will drive the majority of decisions and occasionally call advisors (regular llms) for help benchmarked them on our shortcut bench (financial modeling in spreadsheet), luna max based agents do ~80% honestly tried pretty hard to make this work, but best i got was ~20%, which is miserable astra and i tried maybe 20 different variants, played with the decision space of jev, did context engineering, tried to parallelize vs serialize the decision making, etc. my jev-native agents can overfit certain tasks, but generalize poorly i'm still very excited about jev, but my current feeling is that jev will be very useful for certain bounded tasks where latency or cost matters a lot, and as a tool in the classic agent loop if people see counter examples where jev is actually useful to drive the *core agent loop*, i'd appreciate some pointers feels like i have to relearn the bitter lesson every 3 month or so