Agentic AI Autonomy Assessment: A Decision-Support Framework Towards Governed Supply Chain Systems
Supply chain decision-making is rapidly transforming with the rise of agentic AI - highly autonomous systems that can operate on complex, long-horizon tasks. Yet the adoption of agentic systems outpaces their governance: existing taxonomies of autonomy only offer discrete classifications, rely on subjective judgement, and cannot track autonomy across a system's life cycle, leaving enterprises unable to assess the risks of increasingly autonomous supply chain agents. This paper proposes the Agentic AI Autonomy Assessment (AAAA) framework, which defines and measures the degree of autonomy at a task level. The framework is based on the three dimensions of user delegation, consultation, and collaboration, enabling continuous monitoring from an agent's development through its runtime to end-of-life. The framework's construct validity was tested in a simulated beer distribution game, examining how the autonomy score relates to a company's performance. Results reveal a weak link between autonomy and tier costs with a positional effect: upstream tiers benefit from higher autonomy while downstream tiers are harmed, positioning autonomy as an inherent dimension of agentic systems, orthogonal to capability. The framework provides a foundation for risk assessment, governance, and transparent autonomy policies to support the governed enterprise adoption of agentic AI in supply chains.