The Autonomy Accountability Framework: A Governance Standard for Autonomous AI Agent Systems

AI agent systems are evolving from tools into autonomous operational actors capable of executing actions across financial systems, enterprise infrastructure, and customer environments. As autonomy expands, governance complexity increases disproportionately. Industry development practices have largely prioritized capability and automation speed, while accountability architecture often remains underdeveloped. This paper proposes the Autonomy Accountability Framework, a governance model for understanding and managing accountability in autonomous AI agent systems. The framework introduces five core constructs: the Autonomy-Accountability Curve, the Agent Accountability Gap (AAG), Governance Debt (GD), the Agent Accountability Stack (AAS), and the Autonomy Accountability Index (AAI). Together they describe the structural governance challenges created by autonomous systems and outline an operational architecture for addressing them. The framework is operationalized through the Autonomy Accountability Index (AAI), a governance evaluation model designed to measure the maturity of accountability infrastructure within agent systems. AAI provides a structured lens for assessing governance readiness as autonomy scales. This work proposes a foundation for governance standards in autonomous AI agent systems and establishes a methodology for future benchmarking and ecosystem analysis. The framework can also be interpreted as a governance maturity model describing how accountability infrastructure must evolve as autonomous AI systems scale.