As of nearly 2026, AI agents can perform complex tasks but lack memory, meaning they can't recall past interactions. This shortfall limits their ability to learn and adapt. The article breaks down three types of memory essential for improving agent performance: Session Memory, User Memory, and Learned Memory. Session Memory captures the immediate context of a conversation, but it vanishes once the session ends. User Memory retains specific information about a user across sessions, such as preferences and goals. While this is useful, it doesn't equate to learning. Learned Memory, on the other hand, allows agents to accumulate insights over time, improving their responses not just for one user but for all users. The author emphasizes that learning for agents is fundamentally about remembering what worked in previous interactions. Without memory, agents start fresh each time, leading to repeated mistakes and missed opportunities for improvement. The article outlines how to implement memory systems, starting with Session Memory, followed by User Memory, and finally, Learned Memory, which allows agents to save and apply general insights. A crucial part of ensuring quality in what agents learn involves a "human-in-the-loop" approach, where proposed insights require user approval before being saved, preventing irrelevant information from cluttering the knowledge base. For developers, the article includes code snippets demonstrating how to build these memory systems. Using specific libraries like Agno, developers can set up agents that remember conversation history and user preferences while also accumulating broader knowledge. The focus is on creating a more capable and responsive AI that evolves through interactions, rather than relying solely on model updates.