At AI-native companies the distance between a customer's report and a change in the product is collapsing. Agents write most of the code and the question a product leader answers has moved from "can we build it" to "should we, and did it work". At these companies customer feedback is starting to answer both, with very few people in the loop. What customers say in support tickets, customer calls, app-store reviews, Reddit, X and community threads is read in one place. A problem is detected from that feedback as it arrives, and the detection opens the work with the evidence attached. An agent makes the fix and a person reviews it. The shift in customer feedback after the release helps verify product changes, and the customers who reported the gap are informed, strengthening retention. Key takeaways - The different touch points where customer context is being used in building self-improving product experiences. - How AI-native teams are retaining customers by closing the loop with those who reported bugs and requested changes, after product changes, at scale. - How companies are reducing the number of humans in the loop for product improvement from customer feedback.