LangChain 把 Jev 接进自家 agent 循环的三个用法
LangChain 演示把 Jev 用于判断任务难度、运行时拦截高风险工具调用、以及按评分标准做在线评估，PII 检测 Jev 秒回且准确率 98%。
LangChain put Jev inside their own agent loop and showed the three jobs they hand it In the demo an LLM took about 5 seconds to say whether a text contains PII, and Jev answered immediately with 98% So their coding agents can ask Jev how hard a task is and save the powerful model for the hard ones Their auto mode middleware asks Jev whether a tool call is risky and blocks it at runtime, a check she had switched off because it used to be too slow And the same model grades an agent's answer against a rubric in online evals: correct, matches the reference, grounded, cites a source My guide points that setup at research sources, with a 0 to 3 relevance rubric over 50 papers and a review queue for the low-confidence calls It also checks which useful papers the filter quietly archived Watch the session, then grab the starter and the prompts below
这条是引用着另一条写的：x.com/alphabatcher/status/2101725422517997896