Multi-Agent AI Systems with LlamaIndex: Collaborative Agent Workflows

Single language model prompts often fail when tasks require specialized reasoning, memory, or tool usage. Building a network of specialized, collaborative AI agents is the modern way to solve complex automation challenges. This text-based course guides you through the foundational concepts of agentic workflows, taking you from basic prompts to structuring multi-agent systems where specialized agents communicate, share state, and solve problems together. What you'll learn: Understand core agentic concepts, including tool calling, memory management, and reasoning loops; Configure specialized LlamaIndex agents with distinct roles, system prompts, and toolsets; Implement multi-agent orchestrations, allowing agents to hand off tasks and collaborate dynamically; Apply modern Python patterns, including asynchronous workflows (async/await) for efficient agent communication; Integrate external data sources to give your agents retrieval-augmented generation (RAG) capabilities; Debug and trace agent decisions to optimize performance and handle edge cases in communication. The course starts with essential terminology and the mechanics of a single agent before moving step-by-step into complex multi-agent handoffs and state management. You will read detailed explanations and analyze clean code snippets to build your understanding. Designed for developers and AI enthusiasts who want to move beyond basic API calls, this course starts with the absolute basics and requires no prior experience with LlamaIndex. Start reading today to build smarter, collaborative AI systems.