Knowledge graphs (KGs) have emerged as a transformative technology for enhancing AI agent memory in cloud database operations. This position paper argues that KGs, particularly causal knowledge graphs (CKGs), serve as unified agentic memory systems that address critical limitations of current approaches including vector search and long context windows. We present evidence from recent research showing that KGs improve context retention, relational modeling, and temporal reasoning in large language model (LLM) agents. Our analysis demonstrates how KGs enable autonomous cloud database operations through enhanced query optimization, root cause analysis, and remediation recommendations. We propose a comprehensive six-dimensional classification framework for memory types across temporal, functional, granularity, update frequency, distribution, and hybrid integration dimensions, showing how KGs support all memory categories while providing structured, causal reasoning capabilities essential for complex database operations.