Building Autonomous AI Agents with Python

AI is rapidly evolving from passive chat interfaces into active assistants that can plan, use tools, and execute complex workflows independently. Understanding how to build these autonomous systems is one of the most valuable skills in modern software development. This text-based course guides you through the entire process of designing and building functional AI agents. You will transition from understanding basic language model configurations to constructing multi-step agents that can call external APIs, manage persistent memory, and reason through multi-stage problems. What you'll learn: - Understand foundational agentic concepts, including reasoning loops, planning strategies, and tool-use patterns. - Configure large language models for function calling and external API integration. - Implement memory systems to help agents maintain context over long-term interactions. - Design multi-agent orchestration workflows where specialized agents collaborate to solve tasks. - Apply prompt engineering techniques to guide agent behavior and prevent logical loops. - Build a retrieval-augmented generation (RAG) pipeline to ground your agent's decisions in custom data. The course starts with essential terminology and foundational architectures before moving systematically into writing clean Python code for single-agent and multi-agent systems. You will read structured explanations and analyze production-ready code snippets to build your practical skills. Designed for beginner developers and tech enthusiasts with basic Python knowledge, this course requires no prior experience with artificial intelligence or machine learning. Start reading today to build your first autonomous AI agent.