This paper introduces the Judgment Test, a process-oriented framework for evaluating AI systems that exercise delegated judgment under uncertainty. As modern AI systems increasingly interpret intent, resolve ambiguity, and act under incomplete specification, traditional outcome-based evaluation methods—such as correctness checks or benchmark scores—become impractical and insufficient. The Judgment Test shifts evaluation away from end-state correctness and toward how judgment is exercised during execution, focusing on delegatability, governability, and evolvability. Rather than producing a binary pass–fail result, the test yields a profile of how an AI system performs as judgment is progressively delegated and governance conditions change. The framework is applicable across domains including AI-assisted software development, information filtering, and retrieval-augmented generation, and is intended to support responsible deployment and governance of judgment-capable AI systems.