Build AI Agents with Python: LangChain, RAG, and Multi-Agent Systems

The demand for AI engineers capable of building sophisticated, context-aware applications is growing rapidly. Master the foundational engineering skills needed to create autonomous AI systems that interact, reason, and solve problems. This course guides you through creating robust AI agents using Python and leading frameworks like LangChain and AutoGen. By the end, you will have the practical knowledge to transition from simple Large Language Model calls to designing complex, production-ready multi-agent architectures. What you'll learn: * Understand the core concepts of AI agents, memory management, tool use, and effective prompt engineering. * Apply Retrieval-Augmented Generation (RAG) techniques to ground LLM outputs in external, reliable data sources. * Build agents capable of complex reasoning and planning using state management frameworks like LangGraph. * Practice developing multi-agent collaboration systems using frameworks like AutoGen to solve multi-step problems. * Configure robust Python development environments and implement basic testing strategies for agent reliability. * Design production-ready agent workflows focusing on error handling and performance considerations. We start with fundamental terminology and setting up your environment, then move step-by-step through practical implementations of RAG, single-agent systems, and finally advanced multi-agent interactions. This course is for beginners in AI engineering or developers familiar with Python who want to apply their skills to building intelligent agents. No prior machine learning or deep learning background is required. Start building your first generation of intelligent AI applications today.