As autonomous AI agents are increasingly deployed in industry, it is essential to safeguard them. We introduce a novel framework that automates the translation of unstructured design documents into verifiable, real-time guardrails. We introduce "Policy as Prompt," a new approach that uses Large Language Models (LLMs) to interpret and enforce natural language policies by applying contextual understanding and the principle of least privilege. Our system first ingests technical artifacts to construct a verifiable policy tree, which is then compiled into lightweight, prompt-based classifiers that audit agent behavior at runtime. We validate our approach across diverse applications, demonstrating a scalable and auditable pipeline that bridges the critical policy-to-practice gap, paving the way for verifiably safer and more regulatable AI.
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