The post argues that most production AI agents lack audit trails and safety, proposing Jev as an independent judge for model output to build a trustworthy execution stack.
99% of AI agents in production are fundamentally broken one model writes the plan, approves its own tool calls, evaluates its own output, and ships it. zero audit trail. zero safety. the stack that fixes it: llm writes. jev judges. code decides.
Gregor Zunic showcases an open-source browser agent combining Browser Use and Jev, finding flights in 7 seconds for $0.0039, using a new action space per step, DOM state space, and small LLM fallback for typing.
Scott Williams predicts custom Jev-style models (parallel constrained decoding) will make existing company agent systems more token efficient, creating millionaires.
The user praises the JEV model for its extremely fast reproduction of each movement and command, believing bots will become smarter as a result.
