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.
Prompting can improve a single run, but it cannot prove that an agent workflow is reliable. This article explains how traces, scorecards, offline evals, and online monitoring turn agent quality into an engineering discipline.
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.