Paul Bryant • 9/13/2026

AI Feedback Is Not Learning: Governing Memory and Model Updates

This article argues that AI agent feedback must be separated from permission to change behavior. It distinguishes stored incident memory, revised runbooks, and updated model weights as different change mechanisms, each requiring its own owner, evaluation, release boundary, and rollback path. Using a checkout incident scenario, it shows why preserving context and testing proposed lessons against non-applicable cases matters before treating feedback as learning.

0 comments

#software architecture #AI Agents #Feedback Loops