The Agent Loop is the foundation of every LLM-based agent system. It shifts the perspective from “prompting a model” to designing a controlled execution cycle where goals, context, actions, and observations interact in a structured loop. This sub-series walks through how agents decide what to do, how they use tools, and how they determine when to stop.

Beyond the basics, it focuses on what makes agents reliable in practice: clear control flow, explicit stopping conditions, robust error handling, and observable behavior through tracing. It also introduces evaluation as a first-class concern, showing how to measure not just outcomes, but the quality of decisions along the way.

The core idea is simple but powerful: agents are not magical. They are systems. And once you understand the loop, you can design, debug, and improve them with engineering discipline rather than guesswork.