A demo proves an agent can succeed once. It says almost nothing about how often it will fail under real load, real input distributions, and real adversarial garbage. The failures that cost you in production are predictable before release — but only if you test the things that actually shift between the demo and the deployment. Four pre-release signals that forecast production failure, and the ones that don’t.
A model grading your agent’s output is the only thing that scales for subjective quality — and it’s a biased instrument you’re reading as a ruler. Here are the biases that actually move scores (position, verbosity, self-preference, leniency), why raw agreement with a human hides them, and how to validate and harden a judge with code — including why you should be reporting Cohen’s kappa, not accuracy.
“It works” is a demo result, not a measurement. An agent is a trajectory, not a function, and grading only the final answer throws away most of what decides whether it’s reliable. Here’s a five-layer scheme for what to measure — outcome, trajectory, cost, failure class, and stability under nondeterminism — with a small harness that computes it.