ML engineers preparing RLHF or DPO training runs need to ensure their chosen/rejected preference pairs are internally consistent, free from labeler bias, and representative of the target capability distribution — but auditing thousands of pairs manually is infeasible. This agent runs automated quality checks and flags problematic pairs before training.