CME295 Lecture 8 starts from the fact that human rating is slow and expensive and BLEU/ROUGE can't recognize a paraphrase. It covers how LLM-as-a-Judge works, three biases (position, verbosity, self-enhancement) and six best practices, splits agent failures into tool prediction, tool execution and response generation, and closes with what MMLU, AIME, SWE-bench, HarmBench and τ-bench each measure, plus pass^k and Goodhart's law.
11-768's Assignment 2 has students use one fixed judge, Qwen3-VL-30B-A3B, to flag four error families in every run of a data-visualization agent, graded by the mean MCC across families on a private set (30% of the assignment). The second half packages students' own tasks as Harbor environments, with one wrong solution the verifier rejects and one that fools it. The theme: the grader you write becomes the RL reward later.
Galileo connects dataset experiments, LLM/code/Luna evaluators, production traces, and runtime guardrails. It fits enterprises that need observability plus intervention, but the old Protect surface is deprecated and evaluator models do not replace human calibration or application security.
Patronus AI treats evaluators as reusable scoring units, then applies them to offline experiments and production traces. It suits teams that want managed hallucination, safety, and multimodal evaluators, but judge scores cannot replace human labels, deterministic tests, or real security validation.