Structural Definition of Artificial General Intelligence

The pursuit of Artificial General Intelligence (AGI) is hampered by a persistent lack of a rigorous, operational definition, leaving the field reliant on aspirational descriptions and narrow behavioral benchmarks. This paper argues that a viable path forward requires moving beyond functional mimicry to a formal structural definition. We propose a framework that defines AGI not by what it can do (i.e., pass a specific test), but by what it is: an integrated cognitive architecture composed of a set of core, non-negotiable properties. We deconstruct general intelligence into its fundamental architectural components: a hybrid reasoning engine supporting logical, intuitive, and simulative cognition; a memory system designed for both long-term knowledge conservation and dynamic adaptation; intrinsic agency enabling autonomous goal-setting and execution; and metacognitive capabilities for self-reflection and continuous improvement. We posit that these properties are not an optional checklist but form an interdependent system where intelligence emerges from their synthesis. This structural approach provides a more robust foundation for the field than existing definitions. It establishes a clear engineering blueprint for constructing AGI, offers a principled methodology for evaluating progress based on architectural milestones rather than task-specific performance, and reframes the discourse from chasing an elusive concept to building a verifiable system.