Designing AI Agent Architecture and Memory Systems

Artificial intelligence is moving rapidly beyond static prompts toward autonomous agents that can plan, remember, and act. To build these systems successfully, you must understand how their internal components communicate and how they retain information over time. This text-based course guides you through the foundational design of modern AI agents, explaining how they perceive external environments, coordinate planning modules, and leverage advanced memory systems to deliver consistent, intelligent behavior. What you'll learn: - Understand the core components of autonomous agent architecture and their interaction patterns. - Analyze how agents perceive environmental inputs and translate them into structured actions. - Implement short-term and long-term memory systems, including semantic storage with vector databases. - Explore retrieval-augmented generation (RAG) patterns to ground agent responses in real-world data. - Design robust state-management flows to maintain agent context across multi-step tasks. The course begins with essential definitions of agentic workflows and foundational architectural concepts. You will then progress through detailed written explanations and conceptual code examples that illustrate perception, planning, tool integration, and memory persistence. This course is designed for aspiring software engineers, product managers, and tech enthusiasts who want to understand the mechanics of autonomous systems. No prior experience with AI agent frameworks is required, making this the perfect starting point for beginners. Start reading today to master the architectural patterns shaping the future of artificial intelligence.