智能体人工智能自主性评估：面向受治理供应链系统的决策支持框架
Agentic AI Autonomy Assessment: A Decision-Support Framework Towards Governed Supply Chain Systems
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中文总结 AI 辅助
本文针对智能体人工智能在供应链决策中应用但治理不足的问题，提出AAAA框架，基于用户委托等三维度衡量任务自主性，经模拟游戏测试结构效度，揭示自主性与层级成本关系，为供应链智能体风险评估等提供基础。
中文摘要 AI 辅助
随着智能体人工智能（能够执行复杂长期任务的高度自主系统）的兴起，供应链决策正在迅速转变。然而，智能体系统的采用速度超过了其治理速度：现有的自主性分类法仅提供离散分类，依赖主观判断，且无法跟踪系统生命周期内的自主性，这使得企业无法评估日益自主的供应链智能体的风险。本文提出了智能体人工智能自主性评估（AAAA）框架，该框架在任务层面定义并衡量自主性程度。该框架基于用户委托、咨询和协作这三个维度，能够从智能体的开发阶段到运行时直至生命周期结束进行持续监控。在模拟啤酒分销游戏中测试了该框架的结构效度，研究自主性得分与公司绩效之间的关系。结果显示自主性与层级成本之间存在微弱联系且有位置效应：上游层级从更高的自主性中受益，而下游层级则受到损害，这表明自主性是智能体系统的一个固有维度，与能力正交。该框架为风险评估、治理和透明的自主性政策提供了基础，以支持企业在供应链中采用受治理的智能体人工智能。
英文摘要
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.
