The paper describes a novel approach to cognitive architecture exploration in which multiple cognitive architectures are integrated in their entirety. The goal is to increase significantly the application breadth and utility of cognitive architectures generally. The resulting architecture favors a breadth-first rather than depth-first approach to cognitive modeling by focusing on matching the broad power of human cognition rather than any specific data set. It uses human cognition as a functional blueprint for meeting the requirements for general intelligence. For example, a chief design principle is inspired by the power of human perception and memory to reduce the effective complexity of problem solving. Such complexity reduction is reflected in an emphasis on integrating subsymbolic and statistical mechanisms with symbolic ones. The architecture realizes a “cognitive pyramid” in which the scale and complexity of a problem is successively reduced via three computational layers: Proto-cognition (information filtering and clustering), Micro-cognition (memory retrieval modulated by expertise) and Macro-cognition (knowledge-based reasoning). The consequence of this design is that knowledge-based reasoning is used primarily for non-routine, novel situations; more familiar situations are handled by experience-based memory retrieval. Filtering and clustering improve overall scalability by reducing the elements to be considered by higher levels. The paper describes the design of the architecture, two prototype explorations, and evaluation and limitations.