Can TypeSafe's Jev Make AI Agents Safer Without Another LLM?

TypeSafe's Jev model is presented as a safety mechanism for AI agents that execute real-world actions. It functions as a "decision-first" model to catch semantically incorrect but structurally valid tool calls, such as issuing a refund for the wrong amount. The proposed safety architecture prioritizes hard-coded rules to handle clear-cut cases first before any model is involved. Jev is then used to evaluate the remaining ambiguous situations, providing a probabilistic decision to allow, block, or send for human review. This layered approach ensures the application owns deterministic boundaries, Jev handles fuzzy judgments, and any uncertainty defaults to human oversight.