用 Jev 把 agent 决策层从大模型剥离，月成本 40 万降到 1200 美元
作者讲一套 Jev Engineering 架构：Gatekeeper、Router、Score/Block/Approve 组成管理决策层，只有真正需要推理时才调用昂贵模型，速度 +19000%、成本 -99.7%。
A GUY WAS SPENDING $400K A MONTH JUST TO ASK "WHICH WORKER GOES NEXT." HE CUT IT TO $1200. not a typo. every "route this," every "is this done," every "should we retry" was going through a full frontier model, at frontier prices, for a call that takes milliseconds to actually decide. he didn't shrink the model. he pulled it out of the decision layer entirely. Speed: +19,000% Costs: -99.7% Jev Engineering turns a pile of AI agents into a real management system - and cuts the heavy, expensive model out of every decision loop. how the architecture works: Gatekeeper Request Mini-Model Validation Tool Router State Worker the management layer never writes a paragraph. it directs, evaluates, blocks, or permits. Route → Score → Block → Approve the expensive model only gets called when the task actually needs one. that's the whole idea behind Jev Engineering: decouple reasoning from deciding, and make the deciding layer fast, cheap, and something you can actually measure. detailed instructions in the article below ↓
这条是引用着另一条写的：x.com/0xkerazcity/status/2102042835352428590