Building an AI Agent Decision Layer with Jev and LangGraph

说明与用途

文章说明如何将Jev决策模型集成到LangGraph代理中，以处理路由和安全决策，替代LLM的结构化决策。

问题

AI agents often rely on a large language model (LLM) to make every decision, even those with a small, well‑defined answer space, leading to unnecessary cost and complexity.

结果

Replacing LLM‑based decision making with Jev reduces cost, improves clarity, and enables a cleaner contract between application code and decision logic.

工作流

• Define state and typed questions for Jev
• Integrate Jev into LangGraph nodes (router, safety, etc.)
• Use Jev’s Choice to select workflow path
• Use Jev’s Noul to assess safety probability
• Apply deterministic prefilter checks
• Route to executor or human review based on decisions

证据

Jev is designed to solve exactly this problem. It's not a traditional LLM, it's a System One decision model: you send a state and a set of typed questions, and it returns structured answers with calibrated probabilities, without generating any text.

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The agent can move forward, stop, ask for human input, execute an action, verify the result, or return to the generator and try again.

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Instead of asking an LLM to produce something like: "I think we should probably execute the request..." and then parsing that text, the application can ask Jev a Choice question whose possible results are already defined.

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