{"slug": "agentic-memory-system-design", "title": "Agentic Memory System Design", "summary": "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.", "body_md": "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.\n\nWithout memory, an agent mostly depends on its current conversation. Once that context is gone, it cannot easily use what happened before.\n\nMemory allows an agent to remember useful information and bring it back when it is needed.\n\nProcedural memory tells the agent **how to do something**.\n\nIt 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.\n\nSemantic memory stores **important facts**.\n\nFor example, if a user says, “There is a hackathon next week,” the system can store a simple fact like **“Hackathon next week.”**\n\nThis keeps the useful information without storing the entire conversation. Semantic memory can be stored and retrieved using a vector database.\n\nEpisodic memory stores **what happened before**.\n\nIt can contain previous conversations, agent responses, activities, and other past interactions. Think of it as the agent’s history.\n\nEpisodic memory can become very large as the agent keeps interacting with users. Giving the complete history to the LLM every time is not efficient.\n\nThat is why useful information from episodic memory can be summarized and converted into smaller facts for semantic memory.\n\nInstead of summarizing memory after every interaction, we can introduce a **gate or threshold**.\n\nFor 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.\n\nThis gives us better control over **what to store, how to store it, and when to store it**.\n\nGood agent memory is not about remembering everything. It is about remembering the **right information at the right time**.\n\nProcedural memory tells the agent how to work, semantic memory tells it what it knows, and episodic memory tells it what happened.\n\nTogether, these make an AI agent much more useful and capable over time.\n\nif you like reading drop a like :D", "url": "https://wpnews.pro/news/agentic-memory-system-design", "canonical_source": "https://dev.to/nimay_04/agentic-memory-system-design-5892", "published_at": "2026-08-19 19:38:18+00:00", "updated_at": "2026-08-19 20:14:50.501410+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "large-language-models", "ai-infrastructure"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/agentic-memory-system-design", "markdown": "https://wpnews.pro/news/agentic-memory-system-design.md", "text": "https://wpnews.pro/news/agentic-memory-system-design.txt", "jsonld": "https://wpnews.pro/news/agentic-memory-system-design.jsonld"}}