Beyond Prediction: A Cognitive Architecture for AI Systems Capable of Reasoning, Planning, Learning, and Autonomous Decision-Making

Contemporary AI systems exhibit strong predictive and generative capabilities, yet autonomous performance in dynamic environments also requires integrated reasoning, planning, learning, and action. This paper proposes a cognitive architecture that organizes these capabilities into a closed loop: perceptual inputs inform structured representations and memory; reasoning and goal management guide hierarchical planning; action outcomes update the system’s knowledge and future decisions. The architecture combines neural methods for learning from data with symbolic representations for structured inference and interpretable planning. It further treats uncertainty estimation and metacognitive monitoring as mechanisms for adapting behavior when knowledge or environmental conditions are incomplete. By connecting continual adaptation with explicit planning and evaluation, the proposed framework offers a conceptual basis for AI systems designed to act reliably beyond prediction alone.