Mastering AI Agents: Practical Development of LangGraph, RAG, MCP, and Multi-Agents

In the era of generative AI, simply writing prompts is not enough to solve real-world business problems. In actual services, you must be able to design AI systems that can make autonomous decisions, call tools, retrieve data, and facilitate collaboration between multiple agents. This course is a practice-oriented program designed for everyone, from developers designing AI agents for the first time to engineers looking to build production-ready systems. You will learn step-by-step, covering agent architecture design using LangGraph, State Management, Tool Calling, API and MCP integration, natural language database control, RAG implementation, semantic search, Few-shot Learning, Reflection, and Fine-tuning. Furthermore, you will master multi-agent collaboration structures, Supervisor patterns, Actor-Critic strategies, asynchronous communication, automated evaluation systems, and Quality Gate construction through hands-on projects. Finally, you will learn MLOps and AI Agent operation strategies essential for real-world production environments, including Metrics/Logs/Tracing-based monitoring, Shadow Deployment, Canary Deployment, Drift Detection, RCA automation, and A/B testing. By the end of this course, you will be able to design, develop, and operate high-performance AI systems applicable to real-world services using the latest AI agent technologies.