1 万次决策的 agent 里，一次 Jev 决策长什么样
Jev 把「下一个该谁上」变成带置信度的类型化 Choice，0.88 高于 0.85 阈值自动放行，1 万次决策成本约 0.42 美元。
this is what one decision looks like inside a 10,000-decision agent your chief of staff asks "which worker goes next?" hundreds of times a day. most people pay a frontier model to answer that. Jev answers it as a typed choice with a confidence score: goal: compare 3 AI-agent tools choice: research confidence: 0.88 above the 0.85 threshold cost: $0.000042 status: saved queue/research below 0.85, it goes to review. no guessing, no paragraph to parse. at $0.042 per 1M input tokens with free output, 10,000 of these cost $0.42. the LLM does the work. Jev decides where the work goes. code executes. full setup + the chief. py router in the article ↓
这条是引用着另一条写的：「长文：如何搭好 Jev 驱动的 agent 技术栈」