Master AI Agents: Hands-On Development with LangGraph, RAG, MCP, and Multi-Agent Systems

In the era of generative AI, it is difficult to solve practical problems simply by writing prompts. In real-world services, you need to be able to design AI systems that can make decisions autonomously, call tools, retrieve data, and collaborate across multiple agents. This course is a hands-on program for everyone from developers designing AI agents for the first time to engineers seeking to build systems that can be operated in production. You will learn step by step, from designing agent architectures with LangGraph to State Management, Tool Calling, API and MCP integration, natural-language database control, RAG implementation, semantic search, Few-shot Learning, Reflection, and Fine-tuning. You will also learn how to build multi-agent collaboration structures, use the Supervisor pattern and Actor-Critic strategies, implement asynchronous communication, create automated evaluation systems, and establish Quality Gates through real-world projects. Finally, you will learn the MLOps and AI Agent operations strategies required in real production environments, including monitoring based on Metrics, Logs, and Tracing; Shadow Deployment; Canary Deployment; Drift Detection; RCA automation; and A/B testing. By the end of the course, you will be able to directly design, develop, and operate high-performance AI systems that can be applied to real-world services using the latest AI agent technologies.