Many AI agents continue operating long after their original assumptions are no longer valid. Data sources change. Policies evolve. Threat models shift.
When auditability is tied only to model training or deployment events, long lived agents become opaque. Decisions can no longer be traced. Changes are poorly documented. Accountability erodes.
Auditability must be continuous, not static. It must capture decisions, changes, and operational behavior over time.
USA-ADL™ embeds auditability into every lifecycle phase. This ensures that agent behavior remains explainable even years after deployment.
Auditability is not about retroactive blame. It is about maintaining trust in systems that operate independently and continuously.
