Most language models are stateless. Each request is processed independently, with no built-in awareness of past interactions or prior decisions. This becomes a major limitation when building AI agents that need to reason across multiple steps, remember context, and improve over time.

This is where agent memory systems come in.

A memory system allows an AI agent to store, retrieve, and use information from previous interactions. Instead of treating every request as a fresh conversation, the agent can recall relevant facts, past actions, user preferences, or intermediate results to guide future decisions.

In this chapter, we will explore how memory systems work, the different types of memory used in AI agents, and how to design memory architectures that allow agents to reason effectively across complex tasks.