用 Jev 做 agent 决策层：快 193 倍、便宜 444 倍
作者实测把工具路由从 LLM 文本生成换成 Jev 结构化决策层，宣称延迟降 193 倍、成本降 444 倍，并给出 LLM 推理 / Jev 决策 / 工具执行的新循环。
THE $0.042 SECRET THAT MAKES JSON-BEGGING LOOK LIKE A JOKE Jev Engineering is the layer most agent stacks are entirely missing. Every production agent eventually hits the same fork: state → multiple possible actions → one route has to win Right now, you are probably renting a poet to do a bouncer's job. You're forcing an expensive LLM to generate text, token-by-token, just to pick a tool. It's slow, it hallucinates schemas, and it burns through your API budget. Instead of sending every routing decision back to a bloated text model, top builders turn that into a dedicated decision layer. The results from our tests: - Up to 193x faster - 444x cheaper - Type errors become mathematically impossible Everyone is obsessing over optimizing the main model. Jev Engineering optimizes what happens between model calls. The new, high-speed agent loop looks like this: LLM -> reason Jev -> decide Tools / Agents -> execute State -> update Jev -> decide again Once decisions become their own sub-second, perfectly typed engineering layer, you stop rationing intelligence. You can finally benchmark, batch, and verify them separately. Full blueprint in the article below ↓
这条是引用着另一条写的：x.com/0xhadari/status/2102413869155061760