AI Agent Fundamentals: Coding Message Structures and Execution Loops

Transitioning from simple prompt-response interactions to dynamic AI agents requires a solid grasp of message structures and execution flows. Understanding how to manage conversational state and coordinate tool calls is the foundation of modern AI engineering. This text-based course guides you through the core concepts of structuring messages and building robust multi-turn execution loops for AI agents. What you will learn: Understand the core architecture of AI agents, system messages, and conversational history; Structure user messages and assistant responses to maintain clean, contextual multi-turn dialogues; Implement mock environments to safely test agent behaviors and tool execution workflows; Collect and format tool calls and execution results for agent evaluation; Apply modern design patterns for structured outputs and state management in agentic workflows; Analyze and refine agent decisions through systematic evaluation of message histories. You will start with the foundational definitions of agentic workflows and message schemas before moving into practical, code-based implementations of execution loops. Through step-by-step written explanations and structured code snippets, you will learn how to design, test, and evaluate a working agent executor. This course is designed for beginner developers and software engineers who are new to AI engineering and want to understand the mechanics behind autonomous agents. No prior experience with AI frameworks is required. Start building your foundation in AI agent development today.