Agentic AI Workflows with LangGraph, CrewAI, AG2, and BeeAI

The shift from single-prompt interactions to autonomous AI agents is transforming how we automate complex business and development processes. To build systems that can plan, reflect, and collaborate, you need to understand the architectural patterns of multi-agent orchestration. This course provides a clear, text-based path to mastering autonomous workflows using the industry's leading frameworks. You will start by exploring the foundational concepts of agentic design, understanding how agents maintain state, make decisions, and interact with external tools and other specialized agents. Next, you will dive into hands-on implementation, reading and analyzing clean code patterns for LangGraph, CrewAI, AG2, and BeeAI. You will learn to structure collaborative teams, manage shared memory, and implement robust error-handling and human-in-the-loop validation patterns. What you will learn: Understand the core architecture of agentic AI, including state management, tool calling, and planning loops; Build structured, graph-based agent workflows using LangGraph to handle complex decision trees; Configure collaborative agent teams with CrewAI to automate multi-step role-based tasks; Implement dynamic multi-agent conversations and negotiation patterns using AG2; Design open, enterprise-ready agent integrations using the BeeAI framework; Apply modern design patterns for memory persistence, rate limiting, and agentic error recovery. This text-only course is structured to take you from foundational agent theory to advanced multi-agent coordination, using clear explanations and practical code walkthroughs. This course is designed for software developers, AI enthusiasts, and tech-savvy professionals who want to build autonomous systems. No prior experience with agentic frameworks is required, though a basic understanding of Python is recommended to get the most out of the code examples. Start reading today and build your first autonomous multi-agent system.