In the previous post we looked at what Jev is, a fast, cheap System One classifier, and used it to triage loan application emails. Here we plug it into a LangChain agent, where it does two jobs well: choosing which model should handle a request, and blocking risky tool calls before they run.
Most of us reach for a large LLM whenever a piece of software needs to "understand" something. Jev, from TypeSafe AI, is a different kind of building block: a fast, cheap, general-purpose classifier that sits between your deterministic code and your big LLMs.
In this post I'll walk through my notes on Jev, where it fits in an architecture, and then build a simple loan-processing intake example you can try in the Playground. A follow-up post covers using Jev inside LangChain agents.