Which agent tasks is Jev good for, and which should stay on an LLM?

Jev fits the narrow, structured decisions inside an agent’s loop: ones with a fixed set of possible answers you can name in advance. TypeSafe’s own use-case map groups these as classification, detection, scoring, routing, search, retrieval, ranking, and verification, with intent classification, fraud detection, tool routing, and citation checks as its own worked examples, each one a question with a closed set of answers rather than open text.

What stays on an LLM is anything that has to produce the reply itself or explain a decision in words. A System One model returns a typed answer and a probability, never a sentence, so the moment a step needs generated text, a summary, a rewritten message, a reason a person can read, it’s an LLM’s job regardless of how narrow the underlying judgment feels. The same cost logic that favors a cheap classifier over an LLM judge is why teams reach for Jev first: run the narrow question at a fraction of the cost, and pay for a generative call only on the step that actually needs one.