Conversational AI systems rarely fail in dramatic ways. They do not crash outright or return obvious errors. Instead, they decay. Conversations lose continuity. Personalization feels inconsistent. Latency creeps upward. Engineers respond by increasing context windows, adding vector stores, or layering more retrieval logic on top. For a while, things improve. Then the same failures return, just at a higher cost.
The uncomfortable truth is that memory, in production conversational systems, is not a model feature. It is state. And state, at scale, behaves like a distributed systems problem, whether teams acknowledge it or not.