AI memory and reasoning systems enable large language models and agents to retain information across interactions and solve complex problems through structured thought processes. Memory systems range from short-term conversation buffers to persistent knowledge graphs, while reasoning techniques guide models through step-by-step problem decomposition, verification, and refinement. Understanding the interplay between memory architecture and reasoning strategy is critical for building production agents that maintain context, reduce hallucinations, and execute multi-step workflows reliably.