Governance Maturity in Autonomous AI Agent Systems

Across evaluated AI agent systems, execution primitives are widely implemented, while governance primitives associated with accountability infrastructure are largely absent at the level of enforced architectural control. Primitive-level distribution is concentrated in execution mechanisms such as exception handling and retry. Termination controls, escalation mechanisms, and human override capabilities appear infrequently relative to execution primitives. At the system level, governance coverage clusters within mid-range distributions, indicating partial implementation of governance primitives without corresponding depth of accountability infrastructure. Variation across system categories reflects differences in execution capability, but not in governance completeness. These findings provide empirical evidence of the Agent Accountability Gap (AAG) within the Autonomy Accountability Framework (AAF). Autonomous systems demonstrate operational capability, but lack consistently implemented accountability infrastructure at the execution layer.