Jev as a Smart Router for Agent Harnesses

Sam Witteveen has published a tutorial demonstrating the integration of Jev into LLM agent harnesses to optimize decision-making loops. The guide details how Jev functions as a conditional router, handling tasks like model selection, risk gating, and tool prioritization more efficiently than standard full-model inference. Key demonstrations include skill progressive disclosure and RAG re-ranking using Jev's logic capabilities. This method aims to lower operational costs and improve response times by filtering requests before they reach expensive foundation models.