Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory
The paper discusses the need for persistent memory in long-running AI agents. It critiques current memory systems and proposes a new framework called Governed Evolving Memory (GEM) to address their limitations. The authors introduce a prototype named MemState to validate their approach and outline future research directions.
- ▪Long-running AI agents require persistent memory for effective learning and decision auditing.
- ▪Current memory systems treat memory as mere storage, leading to several failure modes.
- ▪The proposed Governed Evolving Memory (GEM) framework emphasizes state trajectory correctness over individual records.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.26252 (cs) [Submitted on 25 May 2026] Title:Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory Authors:Abdelghny Orogat, Essam Mansour View a PDF of the paper titled Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory, by Abdelghny Orogat and Essam Mansour View PDF HTML (experimental) Abstract:Long-running AI agents need persistent memory. Memory supports 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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
