Developing Production AI Agents with RAG and MCP

AI development is shifting from static chat interfaces to active, autonomous agents that can query data and interact with external systems. This text-based course guides you through the foundational concepts and modern architectures needed to build reliable AI agents for production environments.\n\nYou will progress from understanding core LLM orchestration to implementing Retrieval-Augmented Generation (RAG) and leveraging the Model Context Protocol (MCP) to securely connect your agents to data sources and tools.\n\nWhat you'll learn:\n- Understand the core terminology of AI agents, RAG pipelines, and MCP architectures\n- Configure vector databases to store and retrieve contextual domain knowledge\n- Connect autonomous agents to external tools and data sources securely using MCP\n- Implement modern orchestration patterns to manage agent state and decision-making\n- Design robust error-handling and fallback strategies for production reliability\n- Practice writing clean, structured agent prompts and system instructions\n\nYou will start with fundamental definitions of agentic workflows before moving step-by-step through vector search, document ingestion, and tool-calling protocols. Through written explanations and practical code scenarios, you will learn how to orchestrate these components into a unified, production-ready system.\n\nThis course is designed for software developers, technical product managers, and AI enthusiasts who want to build functional agents from scratch. No prior experience with agent frameworks is required, though a basic understanding of programming concepts is helpful.\n\nStart reading today to build and deploy your first secure, data-connected AI agent.