Agent Memory
Systems for storing information that AI agents can use beyond the context window – from short-term scratchpads to persistent knowledge stores.
Agent memory enables AI agents to store and use information beyond the context window – for learning, personalized assistants.
Explanation
Agent memory spans multiple levels: Working memory (current task info), episodic memory (past interactions), semantic memory (factual knowledge), procedural memory (learned patterns). Implementation via vector stores, key-value stores, or structured databases.
Marketing Relevance
Critical for personalized, context-aware agents. Without memory: every interaction starts from zero. With memory: agents learn preferences, remember past tasks, improve continuously.
Example
A marketing agent remembers: "Last campaign for product X had 3.2% CTR. Similar audience, so start with similar messaging."
Common Pitfalls
Outdated information can mislead. Privacy concerns with persistent storage. Retrieval errors with large memory stores. GDPR-compliant deletion required.
Origin & History
Memory concepts in AI stem from cognitive science (Atkinson-Shiffrin model, 1968). LLM-specific memory systems became popular in 2023-2024 with MemGPT and LangChain Memory.
Comparisons & Differences
Agent Memory vs. RAG
RAG retrieves external documents; agent memory stores and uses the agent's own experiences and learning progress.