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The piece evaluates TypeSafe AI's Jev, a non-generative "System One" model, as an alternative to code-based and LLM-as-judge agent evaluators. The speed and cost figures (up to 200x faster, 400x cheaper) are vendor claims, and the excerpt doesn't show the accuracy and repeatability results, so readers should check those before treating Jev as a practical evaluator.
Can Jev Be a Better Agent Evaluator?
We tested using Jev-as-a-Judge against LLM judges on accuracy, repeatability, latency, and cost to see whether System One models could offer a new approach to agent evaluation.
Today, agent evals come in two flavors: code-based and LLM-as-judge. Both have their own limitations: code-based evaluators can only be used for a narrow set of problems with set inputs, while LLM judges can be slow, expensive, and unreliable. With the popular release of TypeSafe AI’s Jev, we wanted to see whether the “System One” model might be a new third form of agent evaluator, and the impact it could have on agent engineering. Jev is a new model released by TypeSafe AI. Jev is actually not a traditional LLM; it doesn’t generate text. It’s what the TypeSafe AI team calls a “System One” model: 📖 System One models are a class of AI models built to make fast, structured decisions that software can use directly. A System One model evaluates a state and returns typed answers and probabilities. How an autoregressive LLM and a System One Model (Jev) answer the same question. According to TypeSafe AI, this makes Jev faster and cheaper than…
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