The Minimal Complete Architecture of Agents: Unifying Biological Intelligence, AI, and Physical Observers

Although the concept of the "agent" is central to artificial intelligence and intelligence science, it has long lacked a unified formal definition. This paper systematically analyzes interdisciplinary theoretical frameworks, establishing "agents are open information processing systems" as the first principle. Using a state-space covering method, we derive the Minimal Complete Architecture (MCA) of agents: any agent can be reduced to a combination of five fundamental functions—Input, Memory, Generation, Control, and Output. These five functions constitute a logically self-consistent and irreducible closed loop of information processing. Based on this architecture, we construct a five-dimensional capability space and, through ternary discretization (Null-0 / Finite-1 / Infinite-2), derive a "Periodic Table of Agent Capabilities" comprising 243 forms. This periodic table covers the complete evolutionary spectrum from zero intelligence to omniscience; it not only explains typical systems—including thermostats, biological organisms, and Large Language Models (LLMs)—as well as observers in classical mechanics, relativity, and quantum mechanics, but also predicts theoretical agent forms yet to be observed. Furthermore, the paper unifies and interprets 19 core concepts, such as perception, learning, and attention, as combinations of these five fundamental functions, thereby verifying the universality of the architecture. In particular, from the perspective of functional axioms, this paper reveals the essential isomorphism among biological intelligence, artificial intelligence, and physical observers: they are all information processing systems of varying intelligence levels set by their respective physical or biological constraints.