What usually blocks automation is not that a model cannot talk. It is that the model’s output is not something code can run. Which queue should this ticket enter? May the agent call this tool? Does this content pass moderation? Those answers have to be a finite choice, a score, or a probability. A fluent paragraph still needs a human — or another prompt — to translate it.
JEV inverts that. You give it state the system already has, then ask typed questions. It returns choice, score or noul, not an essay. TypeSafe AI calls this a System One Model: fast, non-verbal, machine-native. The name borrows System 1 from psychology — quick judgment, not slow explanation.
A minimal example: the state is “My card was charged twice. Please fix this urgently.” The questions can be topic (billing / account / technical), urgency (low / medium / high), and whether a human is needed (a 0–1 probability). Downstream code that sees topic=billing, priority=high, needs_human=0.93 can escalate to billing immediately.
JEV is not a chatbot replacement. Use an LLM when you need explanation, long-form writing, or a multi-turn conversation. Use JEV when software must make an explicit judgment at volume. They stack: JEV gates and routes; the LLM speaks only when generation is the job.
Keep three rules: write the judgment as a fixed type; threshold on calibrated probability; escalate when unsure instead of pretending the model already “figured it out.”