把决策从 LLM 调用里拆出来：Jev 决策层宣称 193 倍提速
作者主张 agent 栈缺的是「LLM 推理、Jev 决策、工具执行」这一层，把状态与动作选择交给 Jev 后可单独基准测试，自测最高快 193 倍、便宜 444 倍。
Stop tuning the model. The 193x speedup lives one layer up The layer that's missing from most agent stacks now has a name. Jev Engineering. It handles everything that happens between your model calls. Every agent eventually hits the same fork: > State > Multiple possible actions > One route has to win Most stacks send that decision back to an expensive LLM call. Jev turns that decision into a dedicated layer. The loop becomes: > LLM reasons > Jev decides > Tools and agents execute > State updates > Jev decides again Once decisions become their own engineering layer, you can benchmark them, batch them, and verify them separately from the model. Reported result on their own tests: up to 193x faster, 444x cheaper. The model race is late-stage. The decision-layer race is early. Full blueprint in the article below
这条是引用着另一条写的：x.com/akshay_pachaar/status/2101037514945597645