Nym rebuilds its agent around Jev, a fast classifier model, for speed and cost gains
moyix · x · 2026-09-21
The Nym team details how they rebuilt their agent's decision-making around Jev, a new super-fast classifier model from TypeSafe. Jev takes state, questions, and candidate answers, and outputs typed decisions with probabilities and confidence in parallel rather than token-by-token, trained via "Reinforcement Learning for Calibrated Decisions".
The confidence score is the key primitive:
- Decide when more context is needed
- Ensemble multiple Jev requests
- Escalate to a larger model for review on low confidence
This lets teams use code-level thresholds to decide which decisions to automate and which need a second look, evaluated against their own tasks. Instead of collecting data and training a classifier that quickly hits out-of-distribution cases, you simply describe the decision you need. Nym reports lower generative-model costs and higher speed while keeping safety controls intact, with use cases in guardrails and tool selection.
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