在 agent 前加 Jev 决策门，省约 90% token
每个请求先过 Jev 回答 Noul/Choice/Score 三类封闭问题，只有约 10% 进入前沿模型，作者称质量不降、token 花费降约九成。
I put Jev in front of my agent, and now it cuts about 90% of my token spend without any drop in quality. Here is the setup Most of what an agent gets is a decision. Is this email worth answering, does this ticket need a human, etc. I was paying frontier prices for every one of them. So I added a gate. Every request goes to Jev first. Jev does not write anything. You send it two things: > State: any text, like with any other model > Questions: not open ones, only ones with a known set of answers There are three types of questions: > Noul: yes or no > Choice: one option from a list > Score: a scale from 2 to 10 It answers in 70 to 500 milliseconds, at $0.042 per million input tokens, and output is free. So, how my setup works: 1. A request comes in and goes to Jev, never straight to the main model 2. Jev answers one question: does this need real work 3. If it is confident the answer is no, the request is closed right there 4. If yes, or if it is not sure, it goes to the frontier model. Only that model writes anything About 10% of requests make it through. The other 90% never touch the expensive model, and the ones that matter still get it. The saving comes from what you skip.
这条是引用着另一条写的：x.com/gippp69/status/2103394701063737504