AI systems are rapidly moving beyond analysis and recommendation. Autonomous agents now initiate actions, modify environments, and interact with systems without constant human oversight. This shift fundamentally changes the nature of risk.
Traditional AI governance models focus heavily on models, data, and outputs. While those concerns remain relevant, they do not address the operational reality of agents that persist over time, evolve through updates, and act independently within defined scopes.
Agentic AI requires lifecycle authority. Someone must be accountable for when an agent is created, what it is allowed to do, how it is monitored, how changes are approved, and when it must be retired. Without explicit authority across these stages, organizations lose control long before incidents become visible.
Lifecycle authority is not about limiting innovation. It is about ensuring that autonomous systems remain governable long after their original designers move on. In practice, most AI failures in production are not model failures. They are governance failures.
USA-ADL™ was designed to address this gap by defining governance controls across the full lifespan of AI agents, from strategy and design through operation and decommissioning.
