Writing

Aggregate success rate tells you an agent failed. It will not tell you that two agents with the same score fail in completely different ways. A short look at why per-axis failure profiles are the more useful number.

When two LLM judges from different families disagree at high confidence, the problem is usually the rubric, not the judges. Here is the three-iteration loop that got them to converge, and the one change that actually did it.

End-task success tells you an agent failed, not why. Here is a 5-axis decomposition of tool-use correctness, graded by cross-family LLM judges, with the calibration discipline that makes the verdicts mean something.