I suspect the entire industry uses "auto-raters", where an agent instance is used to scores the agent's output. The idea is similar in intent as using adversarial networks to train image generation, minus the human labelers. Raising the scores of the auto-rater then becomes the metric teams optimize, and it is no wonder the end result is that the agent scores its own generated content the highest.