Jev 五步决策循环拆解：LLM 规划，Jev 选路
TypeSafe 联创 Diogo Almeida 解释 Jev 不做对话智能，只在分支点输出确定性选择，配合 LLM 分析、工具执行与状态更新，比经典 LLM 查询快数倍、便宜数百倍。
I am genuinely surprised why everyone isn't using this yet Diogo Almeida, co-founder of TypeSafe AI, which developed the Jev system, and previously at OpenAI and Google Brain, stated a simple idea: "The point of Jev is not to make the model smarter in a conversational sense, but to make it completely deterministic at decision points. If you have a choice of N actions, the system should produce a precise choice without hallucinations or extra tokens" 1. Analysis and reasoning: A large language model (LLM) performs the initial deep analysis of the task and creates an overall plan of action 2. Decision filtering: At a branching point, a specialized layer (Jev) instantly selects the optimal route from the available options without calling an expensive LLM 3. Execution: External tools or sub-agents perform the selected action in the environment 4. State update: The system captures new data and updates the current context 5. Re-selection: Jev immediately makes the next local decision based on the new state, closing the loop As a result, you get a ready-made, highly accurate decision-making solution for agents that works many times faster and costs hundreds of times cheaper than classic LLM queries More useful projects in the article below
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