I am hosting a free live workshop on Building Decision Layers for AI Agents with Jev. A lot of agent workflows still use general-purpose LLMs for small decisions that do not require generated text. Things like: → Which model should handle this request? → Is this tool call risky? → Is this retrieved result relevant? → Should the agent continue, stop, or ask for human approval? In this workshop, I will explain how Jev can be used as a decision layer around an AI agent, where the model handles bounded semantic judgments while normal code keeps control over the final action. We’ll cover practical use cases including: - model routing - tool-risk gating - retrieval and relevance checks - verification and supervision - escalation and human approval I will also walk through a hands-on implementation and share results from my own experiments comparing Jev with a general-purpose LLM on latency, cost, and decision behavior. The goal is not only to understand how Jev works, but also where it actually fits inside an agent architecture, when deterministic code is still better, and when you still need a full reasoning model. The workshop is completely free, but seats are limited. Book your seat from the link in the comments.