Building AI Agents: A Practical Guide with Python and RAG

Artificial intelligence is shifting from passive chat interfaces to active, autonomous agents that can execute tasks and retrieve real-world data. Understanding how to build these agents is becoming an essential skill for developers and tech enthusiasts alike. This text-based course guides you through the foundational concepts of autonomous AI agents, teaching you how to construct them from scratch. You will transition from understanding basic language model configurations to implementing functional agents that use tools and external knowledge bases. What you'll learn: Understand the core architecture and design patterns of modern AI agents; Implement Retrieval-Augmented Generation (RAG) to connect agents with external data; Write clean Python code to manage agent workflows and state transitions; Configure tool calling so your agents can interact with external APIs; Apply prompt engineering strategies specifically optimized for agent reasoning; Review best practices for agent evaluation, error handling, and security. You will start with key terminology and foundational AI concepts before moving step-by-step through practical Python implementations. The lessons guide you through connecting language models to external data sources and structuring autonomous decision-making loops. This course is designed for beginners, developers, and tech professionals looking to enter the field of AI engineering, requiring no prior experience with AI models. Start reading today to build your first autonomous AI agent.