Jev-as-a-Judge for Agent Evals

TypeSafe AI’s Jev is presented as a “System One” decision model that could offer a third approach to agent evaluation alongside deterministic code-based checks and slower, less consistent LLM-as-judge methods. Rather than generating text token by token, Jev evaluates structured state through typed Choice, Score, and Noul questions, returning answers, probabilities, and confidence values. In an experiment using five fixed weather-agent traces, Jev was compared with GPT-5.6 Luna, GPT-5.6 Terra, and Claude Sonnet 4.6 across 100 repetitions per case, with human labels used as the accuracy reference. Jev matched the human binary pass/fail labels on all 500 repeated decisions, showed substantially lower observed score variance than the LLM judges, and cost $0.00035 per call, suggesting potential for broader and more frequent offline and production evaluation. The results are limited to a small, bounded test and do not establish why Jev was more stable or whether its performance generalizes, while low-cost evaluators could also amplify incorrect judgments at scale; therefore, human review and alignment checks remain important.

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