LangChain- Agentic AI Engineering with LangChain & LangGraph

Eden Marco's course on Agentic AI Engineering is a comprehensive, production-oriented masterclass designed for developers looking to transition from simple prompt engineering to building sophisticated, stateful AI agents. This course dives deep into the architecture of modern agentic workflows, leveraging the power of Python, LangChain, and LangGraph. Students begin by mastering foundational concepts such as Chain of Thought, ReAct, and Few-Shot prompting, before transitioning into advanced territory like state management, complex multi-agent orchestrations, and context engineering. A key highlight of this program is the hands-on implementation of Retrieval-Augmented Generation (RAG) pipelines utilizing industry-standard vector databases like Pinecone and FAISS. Developers will also explore the cutting-edge Model Context Protocol (MCP), learning how to seamlessly connect local data sources and enterprise tools to their language models. Throughout the course, Eden guides learners through the inner workings of the open-source LangChain codebase, enabling them to debug, extend, and deploy robust AI agents with absolute confidence. This is the ideal learning path for software engineers looking to construct resilient, enterprise-grade AI systems.