Agentic AI Engineering with LangChain and LangGraph

Modern AI is moving beyond simple chat interfaces toward autonomous agents that can reason, plan, and execute tasks. This course provides a clear path to understanding and building these sophisticated systems using the industry-standard LangChain and LangGraph frameworks. You will gain the skills to engineer production-ready agentic systems that use external tools and manage complex workflows. By the end of this course, you will be able to design AI architectures that autonomously solve real-world problems. What you'll learn: - Understand the core principles of agentic AI and autonomous systems - Build multi-agent architectures using LangGraph and LangChain - Implement advanced Retrieval-Augmented Generation (RAG) for data-driven agents - Configure tool calling and Model Context Protocol (MCP) integrations - Design complex reasoning workflows with chain-of-thought and ReAct prompting - Develop production-ready AI applications using Python The course begins with foundational terminology and prompt engineering theory before progressing to practical implementation. You will explore how to create search agents and documentation assistants, moving from basic concepts to integrated agentic systems. This course is designed for software developers and data professionals looking to enter the field of AI engineering. While familiarity with Python is helpful, no prior experience with LangChain is required. Start building the next generation of autonomous AI today.