Abstract
The defining challenge of AGI research is to build synthetic systems that genuinely instantiate autonomous agency rather than merely producing sophisticated intelligent outputs. While contemporary Large Language Models (LLMs) achieve remarkable reasoning and statistical capacity, they remain structurally fragile and devoid of any intrinsic stake in their own operational survival. This paper addresses that category error with the Agency Spectrum, a multidimensional diagnostic framework that asks not whether a system is an agent but how the competencies constituting agency are organized within it, across diverse substrates. It decouples a system’s capacities along three independent axes rather than scoring it on a single scale. The framework’s theory draws together Active Inference, the Free Energy Principle, and multiscale biology, with a cross-disciplinary synthesis across philosophy, psychology, biology, and computer science. The result is a substrate-agnostic vocabulary: biological and engineered systems can be compared on common terms. We propose a recursive, closed-loop architecture separating the Driver (intrinsic, viability-grounded goal generation), the Engine (generative modeling), and the Interface (the sensorimotor boundary coupling the system to its environment), in which the three components are joined through bidirectional perception–action exchange. Synthesizing Active Inference with principles of multiscale biology, we show why scaling processing throughput cannot generate autonomous, goal-directed behavior. To reach sophisticated agency, architectures must move beyond disembodied computationalism. We identify three necessary architectural conditions—endogenous normativity, recursive substrate-level viability distribution, and structural remapping capacity—and offer four candidate engineering pathways for transitioning from passive information-processing engines to integrated, self-sustaining synthetic agents.