AI Agentic Reasoning: From ReAct to Multi-Agent Systems

Large language models are evolving from static text generators into active problem-solvers. To build truly autonomous AI applications, you must understand how to structure their reasoning, planning, and collaboration. This text-based course guides you through the core architectural patterns of modern AI agents, helping you transition from writing simple prompts to designing complex, self-correcting workflows and multi-agent systems that can break down goals, use external tools, and work together to solve complex tasks. What you'll learn: - Understand the foundational principles of agentic reasoning, tool use, and loop-based execution. - Implement the ReAct (Reason and Act) framework to combine reasoning traces with action execution. - Apply plan-and-execute strategies to break down large, ambiguous goals into structured sub-tasks. - Design self-correction and reflection loops that allow agents to debug their own outputs. - Configure multi-agent architectures where specialized agents collaborate and share information. - Explore modern orchestration patterns, structured output validation, and basic evaluation techniques for agent reliability. You will start with the absolute basics of agent terminology and single-agent loops before advancing step-by-step to multi-agent collaboration, studying conceptual frameworks and clean code patterns along the way. This course is designed for software developers, tech enthusiasts, and AI beginners who want to move beyond basic prompting. No prior experience with agentic frameworks is required, though a basic understanding of programming concepts is helpful. Start reading today to unlock the potential of autonomous AI orchestration.