Memory

What is Memory?

Memory in agents refers to systems that store, organize, and retrieve information to guide decision-making and behavior. In AI, this often involves structured data like databases, embeddings, or recurrent neural networks that retain past interactions, learned patterns, or environmental states. Agents use memory to contextualize inputs, adapt to new scenarios, and improve performance over time—akin to how reinforcement learning agents build "experience" through trial and error. Memory can be transient (short-term, for immediate tasks) or persistent (long-term, for overarching knowledge), enabling both rapid responses and sustained learning.

This parallels human memory, where biological processes encode, consolidate, and recall information through neural networks. Humans rely on short-term memory for immediate tasks and long-term memory for skills and experiences, with recall shaped by association, emotion, and repetition. However, human memory is reconstructive and prone to biases, while AI memory is typically more precise but lacks the fluid, intuitive generalization of biological systems. By mimicking human memory structures—such as episodic (event-based) or semantic (fact-based) frameworks—agents aim to achieve more natural adaptability, though they often prioritize efficiency over the organic complexity of human cognition.

Types of Memory

• Episodic Memory

• Semantic Memory