Designing and Optimizing AI Agent Workflows

Artificial intelligence is shifting from simple prompt-and-response interactions to autonomous agents capable of executing complex, multi-step tasks. Understanding how to design, coordinate, and optimize these agentic workflows is becoming an essential skill for modern developers and technology professionals. This text-based course guides you from the fundamental concepts of AI agents to building structured workflows that solve real-world problems. You will learn how to connect language models with external tools, manage state, and optimize performance for reliability and efficiency. What you'll learn: Understand foundational agentic architectures, including single-agent and multi-agent design patterns; Configure tools and API integrations to allow AI agents to interact with external data sources; Apply modern retrieval-augmented generation patterns to ground agent decisions in factual data; Design structured orchestration workflows using state management and decision-making loops; Implement basic observability and evaluation techniques to monitor, debug, and optimize agent performance; Practice writing robust system prompts and routing logic to handle edge cases. You will start with essential terminology, exploring how agents plan, use tools, and maintain memory. From there, you will read through step-by-step implementations of routing logic and multi-agent collaboration, learning how to debug and refine your setups for production-ready reliability. This course is designed for software developers, product managers, and tech enthusiasts who want to build autonomous systems. No prior experience with AI agent frameworks is required, though a basic familiarity with programming concepts will help you get the most out of the written examples. Start reading today to master the next generation of AI automation.