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TypeSafe Jev Offers a Lightweight Decision Layer to Streamline AI Agent Workflows

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Most AI agent systems rely on a single large language model for every task, including planning, tool selection, and risk scoring, which makes them costly and hard to test. TypeSafe Jev is a purpose-built tool that handles decision-making over a closed set of outcomes using three typed primitives: Choice, Score, and Noul. These primitives let developers separate fast, frequent decisions — such as tool routing, moderation triage, and permission gates — from open-ended generative tasks, keeping the latter in more capable models. Under this architecture, deterministic application code retains control over permissions, side effects, and rollback, preventing probabilistic components from silently driving destructive actions. The approach makes individual models replaceable while keeping the overall decision protocol stable and observable.

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