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