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This paper introduces a layered translation method to bridge the gap between high-level AI governance standards (e.g., ISO, NIST) and implementable runtime guardrails for agentic AI systems. The method decomposes governance objectives into four control layers: governance objectives, design-time constraints, runtime mediation, and assurance feedback, using a control tuple and enforceability rubric for layer assignment. A procurement-agent case study demonstrates how standards can inform control placement across architecture, runtime policy, human escalation, and audit, reserving runtime guardrails for observable, determinate, and time-sensitive controls.
Existing AI governance standards can be translated into concrete runtime guardrails for agentic AI by strategically distributing controls across architectural layers, human oversight, and automated policies.
Agentic AI systems plan, use tools, maintain state, and produce multi-step trajectories with external effects. Those properties create a governance problem that differs materially from single-turn generative AI: important risks emerge dur- ing execution, not only at model development or deployment time. Governance standards such as ISO/IEC 42001, ISO/IEC 23894, ISO/IEC 42005, ISO/IEC 5338, ISO/IEC 38507, and the NIST AI Risk Management Framework are therefore highly relevant to agentic AI, but they do not by themselves yield implementable runtime guardrails. This paper proposes a layered translation method that connects standards-derived governance objectives to four control layers: governance objectives, design- time constraints, runtime mediation, and assurance feedback. It distinguishes governance objectives, technical controls, runtime guardrails, and assurance evidence; introduces a control tuple and runtime-enforceability rubric for layer assignment; and demonstrates the method in a procurement-agent case study. The central claim is modest: standards should guide control placement across architecture, runtime policy, human escalation, and audit, while runtime guardrails are reserved for controls that are observable, determinate, and time-sensitive enough to justify execution-time intervention.