What you'll learn

Have you ever built an AI agent that forgets everything between interactions, making it impossible to provide consistent, personalized results? In this course, Memory-augmented and Persistent Agents, you’ll learn how to design memory-augmented, persistent agent systems that maintain context across sessions and personalize behavior over time. First, you’ll explore why agents need memory, examining and comparing the limitations of stateless LLM interactions vs. how memory enables learning, continuity, and adaptation in both single-agent and multi-agent workflows. Next, you’ll dive into agent frameworks and memory techniques, comparing how popular tools handle memory management. Finally, you’ll learn how to design and build a multi-agent system with persistent memory capable of: remembering past interactions, accumulating and retrieving knowledge over time, and improving decision-making and behavior across sessions. When you’re finished with this course, you’ll have the skills and knowledge needed to design, implement, and evaluate memory-augmented and persistent agents that maintain context and operate reliably across sessions in real-world applications.