As intelligent systems are developed across diverse substrates - from machine learning models and neuromorphic hardware to in vitro neural cultures - understanding what gives a system agency has become increasingly important. Existing definitions, however, tend to rely on top-down descriptions that are difficult to quantify. We propose a bottom-up framework grounded in a system's information-processing order: the extent to which its transformation of input evolves over time. We identify three orders of information processing. Class I systems are reactive and memoryless, mapping inputs directly to outputs. Class II systems incorporate internal states that provide memory but follow fixed transformation rules. Class III systems are adaptive; their transformation rules themselves change as a function of prior activity. While not sufficient on their own, these dynamics represent necessary informational conditions for genuine agency. This hierarchy offers a measurable, substrate-independent way to identify the informational precursors of agency. We illustrate the framework with neurophysiological and computational examples, including thermostats and receptor-like memristors, and discuss its implications for the ethical and functional evaluation of systems that may exhibit agency.

翻译：随着智能系统在不同基底上的发展——从机器学习模型和神经形态硬件到体外神经培养——理解什么赋予系统能动性已变得日益重要。然而，现有定义往往依赖于难以量化的自上而下的描述。我们提出一种自下而上的框架，其基础是系统的信息处理阶数：即其输入转换随时间演化的程度。我们识别了三种信息处理阶数。I类系统是反应性的且无记忆的，直接将输入映射到输出。II类系统包含提供记忆的内部状态，但遵循固定的转换规则。III类系统是自适应的；其转换规则本身会作为先前活动的函数而改变。虽然这些动态特性本身并不充分，但它们代表了真正能动性的必要信息条件。这种层级结构提供了一种可测量的、与基底无关的方法来识别能动性的信息前兆。我们通过神经生理学和计算实例（包括恒温器和忆阻器类受体）阐释了该框架，并讨论了其对可能表现出能动性的系统的伦理和功能评估的意义。