Building AI Agents: Designing Multi-Agent Systems in Python

Single-prompt chatbots are only the beginning of what artificial intelligence can achieve. By transitioning to agentic AI, you can build autonomous systems that reason, plan, and collaborate to solve complex business problems. This text-based course guides you through the process of engineering sophisticated, coordinated teams of AI agents using Python. You will progress from understanding foundational reasoning frameworks to designing workflows that allow multiple agents to share tasks, use external APIs, and make decisions dynamically. What you'll learn: - Understand the foundational concepts of agentic AI, including autonomous reasoning, planning, and memory. - Implement advanced prompting patterns such as Chain-of-Thought and ReAct to guide agent decision-making. - Design structured agentic workflows using routing, parallelization, and collaborative multi-agent orchestration. - Enable agents to interact with the world by integrating databases, search tools, and external APIs. - Apply retrieval-augmented generation (RAG) and basic vector database patterns to ground your agents in custom data. - Build functional text-based prototypes for multi-agent systems, such as automated planners and task managers. You will start with core definitions and architectural patterns before moving into step-by-step coding exercises. Each module reinforces your understanding through detailed text explanations, structural walkthroughs, and practical Python code snippets. This course is designed for software developers, tech enthusiasts, and beginners looking to transition from basic prompt engineering to building autonomous AI systems. No prior experience with artificial intelligence frameworks is required, though a basic familiarity with Python is helpful. Start reading today to begin building your first collaborative team of AI agents.