20 Agentic Use Cases of TypeSafe AI’s Jev
Last week, TypeSafe AI released Jev, its first System One model. Founder Diogo Almeida previously worked at OpenAI on the instruction-following research behind ChatGPT. Jev does not chat, write code or summarize. It takes unstructured state and returns typed decisions with calibrated probabilities. That makes it a natural fit for the thousands of small judgments inside an agent loop: which model to call, whether a command is safe, which passage is relevant, whether the agent is actually don
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TypeSafe AI released a decision-making model called Jev that returns typed answers with probability scores instead of generated text. Unlike traditional chatbots, Jev takes unstructured information and makes specific judgments suited for agent loops, such as choosing which model to call, evaluating whether commands are safe, or determining if a task is complete. The model works through three primitives: Choice selects one option from a list with probabilities, Score rates information on a rubric, and Noul returns the probability that a statement is true. Jev is designed for thousands of small judgments within AI agents, with applications ranging from routing requests between models to detecting prompt injections and controlling browser or computer interfaces.
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