Building AI Agents and Multi-Agent Workflows

AI is shifting from passive chat assistants to autonomous agents that can plan, use tools, and collaborate to solve complex tasks. Understanding how to build these agentic workflows is becoming an essential skill for modern developers and tech professionals. In this text-based course, you will transition from writing basic prompts to architecting sophisticated, self-directing AI agent systems. You will gain a solid conceptual foundation of agentic design patterns and learn how to implement them using industry-standard frameworks like LangGraph and CrewAI. What you'll learn: - Understand the foundational concepts of AI agents, planning loops, and tool use. - Build single-agent systems capable of executing multi-step tasks autonomously. - Design multi-agent workflows using CrewAI to orchestrate collaborative teams of specialized agents. - Implement state management and persistent memory in agentic loops using LangGraph. - Configure external tools and APIs for agents to search the web, read files, and process data. - Apply modern evaluation and observability patterns to monitor and debug agent decisions. The course begins with the essential definitions and core architectures of agentic systems before guiding you through practical code implementations. You will read clear explanations of design patterns, explore structured code walkthroughs, and learn how to orchestrate complex collaboration between multiple specialized AI personas. This course is designed for software developers, product builders, and tech enthusiasts who want to learn agentic AI from the ground up. No prior experience with AI agents is required, though a basic familiarity with Python is helpful. Start reading today to build your first autonomous AI workforce.