What Is AI Agent Governance? A Lifecycle Framework for Governing Production AI Agents

AI agent governance is presented as the centralized oversight needed to ensure production agents can be shown, at any time, to be authorized to perform a specific action for a specific user on a specific resource. It applies to systems that use language models to dynamically call tools or APIs against real systems, rather than systems limited to answering questions or generating recommendations. The proposed lifecycle includes registration, permission scoping, versioned deployment and updates, operational auditing, cost accounting, and decommissioning, with agents repeatedly revisiting review and deployment stages as they change. An actions runtime can directly enforce per-action authorization, contextual policy checks, version control, and audit logging, while ownership assignment, cost reconciliation, approval processes, and retirement sign-off require organizational procedures as well. The discussion emphasizes least-privilege delegated access, immutable records that identify the agent, user, action, target, version, and policy decision, and retirement processes that revoke credentials, disable execution paths, halt spending, and retain evidence. It cites standards and regulations including NIST AI RMF, ISO/IEC 5338:2023, and the EU AI Act to argue that operational telemetry alone is insufficient for full lifecycle governance, and describes Arcade.dev as a platform intended to provide runtime controls, registries, credential management, and audit capabilities to support these requirements.

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