Agentic AI Governance Framework

The rapid adoption of Agentic AI systems capable of autonomous reasoning, tool execution, and multi-agent coordination—has outpaced the development of governance mechanisms required to manage their operational, regulatory, and ethical risks. Existing AI governance standards such as NIST AI RMF and ISO/IEC 42001 provide foundational principles but lack implementation depth for continuously acting, multi-agent systems operating across jurisdictions.

This paper introduces the Agentic AI Governance Framework, a practical, platform-agnostic governance model designed to bridge the gap between high-level compliance principles and deployable operational controls. The framework defines six governance principles spanning pre-deployment validation, runtime monitoring, human-in-the-loop escalation, multi-agent accountability, tool-calling access control, and continuous evaluation.

A key contribution of this work is the Agentic Log Retention Index (ALRI), a quantitative heuristic for determining audit-grade log retention based on agent autonomy, risk exposure, and jurisdictional requirements. The paper also provides implementation guidance across common enterprise orchestration environments, including open-source and low-code automation platforms.

This work is intended for enterprise architects, AI governance leaders, compliance teams, and platform engineers responsible for deploying and operating Agentic AI systems in regulated or high-risk environments. The framework is positioned as a reusable baseline for safe, auditable, and scalable Agentic AI operations.