AI agents usually do not fail with a dramatic crash. They fail quietly through wrong tool calls, invalid arguments, retry storms, looping behavior, and weak recovery. This article explains where the agent loop breaks and how to design traces, guardrails, limits, checkpointing, idempotency, and evals that catch incidents before users do.
Learn how to design an eval set for a tool-using agent using trace-level evaluation, dataset splits, layered scoring, and realistic failure cases that catch regressions before production.
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.
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 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.