You built an LLM judge to grade your agent. What grades the judge? An eval you never validated is a ruler you never checked against a meter stick — and a biased judge doesn’t just add noise, it moves your headline number in a consistent direction. How to meta-evaluate a judge: the labeled set, the agreement metric that isn’t accuracy, and the drift check.
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.