Building Agentic AI with LangChain and LangGraph

Artificial intelligence is shifting from simple chat interfaces to autonomous agents capable of planning, reasoning, and executing multi-step tasks. Understanding how to build these agentic systems is essential for anyone looking to stay at the forefront of AI development. This course provides a comprehensive introduction to creating autonomous systems that can iterate on their own performance and handle sophisticated workflows. You will transform your understanding of Large Language Models into the ability to build functional agents that remember past interactions and refine their own logic. By focusing on stateful design and reasoning architectures, you will learn how to move beyond basic prompting into the world of persistent, self-improving AI applications. What you'll learn: - Understand the fundamental concepts of agentic AI and autonomous reasoning patterns. - Configure stateful workflows using LangGraph to manage complex, iterative tasks. - Implement ReAct, Reflection, and Reflexion architectures for self-correcting agents. - Apply memory and persistence patterns to maintain context across long-term interactions. - Integrate retrieval-augmented generation (RAG) and vector databases into agentic flows. - Practice writing clean, modern Python code with type hints for robust AI development. The course begins with foundational definitions and the core logic of agentic design before moving into graph-based workflows and advanced reasoning strategies. It is designed for beginners with a basic grasp of programming who are ready to build their first intelligent agents through written explanations and code-based exercises. Begin your journey into the next generation of autonomous AI today.