Five-judge content agent loop sharpened by Jev-class decision models
The author runs agentic content loops through five judges (semantics, filter, strategy, benefit, editorial), saying Jev-class decision models make the judges sharper and any judge can send a draft back for another round.
Most AI content is AI slop 🤷♂️ Not because the model ignores your rules. It probably follows them. The problem? Most content workflows are linear. Prompt → draft → publish. One pass. No second opinion. Agentic loops change the game. As I always say → quality isn't a function of talent. It's a function of iterations. A proper agentic loop keeps working until the output passes your harness: your rules, your tools, your skills. And here's the trick → judges 🤖 You can plug several of them into the loop. With JEV-class decision models they get even sharper. In my setup every page goes through 5 judges: → semantics → filter → strategy → benefit → editorial Two pages, 10 agents running in parallel. Any judge can send the draft back for another round. That's the key. Not a better prompt. A better loop. Want to learn why it works and how to build it? Join us 👉 https://sensai.io/ 841 people from all over the world are already waiting 🥷