Agentic AI Development with LangGraph and LangChain

Traditional AI models follow fixed paths, but the next generation of software relies on agents that can reason, use tools, and collaborate. This course provides a clear path into the world of agentic AI, where you move beyond simple prompts to building autonomous systems that can think and act. You will transition from understanding basic language model interactions to architecting sophisticated agentic applications. By mastering the frameworks of LangGraph and LangChain, you will learn how to give AI the ability to manage its own state, use external tools, and work in teams to accomplish goals through written logic and structured workflows. What you'll learn: - Understand the core principles of agentic AI, including reasoning loops and tool-use patterns. - Build stateful AI workflows using LangGraph to manage complex decision-making processes. - Implement memory systems that allow agents to retain context and learn from past interactions. - Design multi-agent systems where specialized AI entities collaborate to solve large-scale problems. - Apply Retrieval-Augmented Generation (RAG) within agentic frameworks for data-driven responses. - Practice writing clean, modern Python code using async/await and type hints for robust agent performance. The curriculum starts with foundational definitions of agents and state management before moving into practical implementation logic. You will progress through reading about single-task agents and move toward orchestrating complex multi-agent environments using industry-standard libraries. This course is designed for beginners interested in the future of AI development; no prior experience with agentic frameworks is required, as all concepts are explained through clear written text and code examples. Start building your first autonomous AI agent today.