Agentic Memory System Design

An engineer detailed the design of an agentic memory system, distinguishing procedural, semantic, and episodic memory. The system uses a threshold to trigger summarization of episodic memory into semantic facts, improving efficiency and control over what an agent remembers.

An AI agent can answer questions using the information available in the current conversation. But if we want the agent to remember users, past conversations, important facts, or previous work, we need a proper memory system. Without memory, an agent mostly depends on its current conversation. Once that context is gone, it cannot easily use what happened before. Memory allows an agent to remember useful information and bring it back when it is needed. Procedural memory tells the agent how to do something . It contains instructions, skills, workflows, or rules that guide the agent. For example, an agent can have a skill file that explains how it should perform a particular task. Semantic memory stores important facts . For example, if a user says, “There is a hackathon next week,” the system can store a simple fact like “Hackathon next week.” This keeps the useful information without storing the entire conversation. Semantic memory can be stored and retrieved using a vector database. Episodic memory stores what happened before . It can contain previous conversations, agent responses, activities, and other past interactions. Think of it as the agent’s history. Episodic memory can become very large as the agent keeps interacting with users. Giving the complete history to the LLM every time is not efficient. That is why useful information from episodic memory can be summarized and converted into smaller facts for semantic memory. Instead of summarizing memory after every interaction, we can introduce a gate or threshold . For example, after 20 activities or conversations, the system can trigger a summarizer agent. The summarizer looks at the recent episodic memory, identifies the important information, and saves it as useful facts in semantic memory. This gives us better control over what to store, how to store it, and when to store it . Good agent memory is not about remembering everything. It is about remembering the right information at the right time . Procedural memory tells the agent how to work, semantic memory tells it what it knows, and episodic memory tells it what happened. Together, these make an AI agent much more useful and capable over time. if you like reading drop a like :D