If an agent fails and you only have the final answer, you are debugging blind. This article explains how useful traces expose the exact step, tool, state, or context failure that actually broke the run.
Most agent failures are not prompt failures. They happen because teams misunderstand the control loop the system is actually running. This article breaks the loop into its real runtime parts and shows why that changes debugging, reliability, and production behavior.
Most confusion about AI agents starts with weak definitions. This article explains the mental model that holds up in real systems: an agent is a control loop around an LLM with tools, state, and observable execution.