Designing AI Agents with LangGraph: Reasoning and Memory

Building intelligent systems requires moving beyond simple prompt chains to autonomous agents that can reason, remember, and make decisions. This text-based course guides you through the architectural principles of designing robust AI agents from scratch. You will transition from writing basic language model calls to architecting complex, stateful systems that can handle real-world workflows. By exploring the core mechanics of reasoning, tool usage, and memory, you will gain the skills to build reliable, production-ready agentic applications. What you'll learn: Understand the fundamental architecture of AI agents, including reasoning loops and memory systems; Implement state management and persistence using LangGraph to maintain context across interactions; Configure tool calling and dynamic routing to allow agents to interact with external APIs; Design multi-agent architectures where specialized agents collaborate to solve complex tasks; Apply modern error-handling and fallback strategies to ensure system reliability. The course begins with essential terminology, basic concepts, and foundational definitions of agentic design before moving into practical code-based structures. You will progress through clear written explanations and step-by-step code analysis to master state graphs and memory patterns. This course is designed for developers, software engineers, and AI enthusiasts who want to build autonomous systems, requiring only a basic understanding of Python. Start reading today to master the architectural patterns of modern AI agents.