Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory

ArXi:2605.26252v1 Announce Type: new Long-running AI agents need persistent memory. Memory s learning across sessions, reduces repeated context injection, and enables auditing of past decisions. Current agent memory systems and database paradigms treat memory as storage. They localize correctness at records, embeddings, or edges. Each supplies only some of the capabilities that long-term memory requires. The result is four recurring failure modes: unregulated growth, missing semantic revision, capacity-driven forgetting, and read-only retrieval.