Building Decision Layers for AI Agents with Jev

​AI agents often use general-purpose LLMs for decisions that do not require generated text: routing a request, judging whether a tool call is risky, checking whether a result is relevant, or deciding when human approval is needed.

​In this free hands-on workshop, I will show how to use Jev as a decision layer around an AI agent. We will look at how Jev’s typed decisions and probability outputs differ from normal LLM generation, then build practical examples where Jev handles bounded semantic judgments while ordinary code keeps control over execution.

​We will cover use cases such as model routing, tool-risk gating, retrieval and relevance checks, verification, escalation, and human-in-the-loop approval. I will also share the results from my own experiments comparing Jev with a general-purpose LLM on latency, cost, and decision behavior.

​By the end of the session, you should understand where Jev fits inside an agent architecture, when it is useful, when deterministic code is still better, and when you should use a full reasoning model instead.

​The workshop is free, but seats are limited.

​Book your seat to join the live session.