Day 3 of my Agentic AI series — Memory in AI Agents 🧠 The biggest shift from “LLMs” → “agents” isn’t just tool calling. It’s continuity. In the Stanford + Google generative agents paper, agents didn’t work because the model was smarter. They worked because the agents could remember, retrieve, and reflect. Three layers of agent memory: • Context window = CPU → what the agent is actively thinking about • KV store = RAM → user preferences + structured state • Vector DB = SSD → semantic long-term memory across sessions And the most important part? Reflection. Not just storing: “John looked stressed today.” But synthesizing: “John has been under pressure lately.” That’s the jump from logging → reasoning. A good agent doesn’t just answer. It accumulates experience. Memory is what turns interactions into learning. And continuity is what makes an agent feel alive. #AgenticAI #LLM #AIEngineering #GenerativeAI #AIAgents MachineLearning ArtificialIntelligence RAG VectorDatabase AI