AutoViewMem is a data-driven framework for organizing long-term conversational memory in AI agents. It creates self-configuring, low-overlap semantic views to improve memory compactness and consistency, and enables focused evidence retrieval without explicit routing or iterative retrieval. This design improves long-horizon question answering and personalization in AI agents.