Agentic AI Foundations: Build RAG and MCP Chatbots

Static AI prompts are no longer enough; the future belongs to autonomous systems that can search, reason, and take real-world actions. This text-based course guides you through the core concepts of agentic AI, helping you transition from writing simple prompts to understanding complex, retrieval-driven architectures. By studying these written lessons, you will understand how to orchestrate AI agents that leverage external data sources and standardized protocols. You will gain the foundational knowledge required to design chatbots that not only chat but also retrieve context and execute tasks dynamically. What you'll learn: - Understand the core architecture of agentic AI systems and how they differ from traditional chatbots. - Learn the principles of Retrieval-Augmented Generation (RAG) to connect AI models with external knowledge. - Explore the Model Context Protocol (MCP) to safely connect models to data sources and tools. - Design logical workflows that allow AI agents to reason, plan, and execute multi-step tasks. - Apply best practices for managing context window limitations and optimizing retrieval accuracy. - Practice designing conversational flows through written step-by-step conceptual exercises. The course starts with fundamental AI terminology and foundational definitions before moving into RAG architectures and MCP integration patterns. You will progress from basic concept breakdowns to reading and analyzing agentic workflow blueprints. This course is designed for beginners, developers, and tech enthusiasts who want to learn the basics of agentic AI without any prior experience in machine learning. Start reading today to build your understanding of the next generation of autonomous AI systems.