Building Multi-Step AI Agent Workflows with Planner-Executor Loops

Modern AI applications require more than simple prompt-and-response interactions. To solve complex, multi-stage problems, developers must build autonomous agents capable of breaking down goals, planning actions, and routing tasks dynamically. This text-based course guides you through the concepts and code patterns needed to construct robust, stateful AI agent systems from scratch. You will transition from basic static prompting to designing sophisticated, adaptive workflows. By understanding the core mechanics of planner-executor loops, you will empower your applications to self-correct, synthesize external data, and make logical decisions at runtime. What you'll learn: - Understand the core architecture of stateful AI agents and planner-executor loops - Implement dynamic routing to direct tasks to specialized tools or sub-agents - Manage application state and memory across multi-step execution cycles - Write clean, structured code to handle model tool-calling and error recovery - Apply evaluation and testing patterns to ensure agent reliability and prevent infinite loops This course begins with essential terminology, architectural diagrams described in text, and foundational design patterns. You will then progress through step-by-step written walkthroughs, analyzing code snippets that demonstrate how to orchestrate planning, execution, and synthesis phases. This course is designed for software developers, engineers, and tech-savvy builders who want to move beyond basic LLM APIs. No prior experience with agentic frameworks is required, though a basic familiarity with programming concepts and API integration is recommended. Start reading today to build autonomous, stateful systems that handle complex workflows with ease.