Agent Memory Infrastructure enables AI agents to retain, recall, and reason over interaction history across sessions, solving the challenge of context continuity in long-running autonomous workflows. These systems persist conversational threads, user preferences, task state, and factual assertions, then apply transformations such as entity and fact extraction, semantic deduplication, temporal decay, conflict resolution, and context-window assembly to surface relevant memories at inference time. Common architectural patterns include vector-based episodic retrieval, graph-structured knowledge consolidation, and hybrid stores combining semantic and relational indexing. The category has evolved rapidly from simple key-value caches and stateless prompt injection toward dedicated memory layers with entity resolution, automatic summarization, and policy-driven forgetting, reflecting the shift from single-turn chatbots to persistent, multi-session agents deployed in customer support, personal assistance, and enterprise workflow automation.