AI agents work best when reasoning and memory are separate. Before each session, the system pulls relevant user context from memory and feeds it into the agent's prompt so it starts informed. During conversations, new preferences like coffee orders get stored asynchronously and retrieved when the agent needs to act.
You need to separate memory from the agent itself. Think of it like this: the agent is the brain that reasons and acts. Memory is a separate layer that gives the brain continuity. They talk to each other, but they're not the same thing. Here is what the production architecture looks like. Here is how it works in production. At session starts, before the agent sees the user's first message, You query the memory layer, pull the relevant context for the user and past preferences, inject a compact structured summary into the system prompt, and the agent starts informed, not blank. During the session, as the conversation reveals new information, for example, this user reveals they like flat white coffee around 10 AM, those facts get written back to a memory layer asynchronously. Highlighting the important information. The agent learns from the conversation what memories are important. The next time your user asks your agent to take an action, it also can retrieve the correct information based on the user memories.
