Reliable agents are not the ones that never fail. They are the ones that fail into the right path. Here is how to classify tool failures into retry, replan, user input, or hard stop, and why retry policy belongs at the tool boundary.
Most bad agent experiences come from bad stopping decisions. Learn how to design stop logic in code with explicit exit states, tool signals, step limits, and traceable runtime policies.
Tool use is where an agent stops generating text and starts affecting real systems. This article explains why tool design acts as both decision boundary and action contract, and how better schemas, validation, and tracing make tool calling agents more reliable.
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 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.