LLM memory systems

Enterprise AI does not fail because models lack intelligence. It fails because models lack memory. Large language models operate from frozen training data while the world continues to change. This structural mismatch creates temporal hallucination, institutional amnesia, and authority collapse in production systems. This article introduces the Real-World Context Bridge, a layered memory architecture that connects static LLMs to dynamic reality. It analyses current research, industry deployments, enterprise implications, and the long-term convergence between native model memory and governed external memory systems. The central argument is clear: memory architecture, not model scale, will determine competitive advantage in applied AI.