Memory as Middleware for Self-Improving AI Agents
AI agents are stateless across sessions by default and therefore operationally amnesic: each session begins with little durable knowledge of prior failures, repairs, preferences, or successful strategies. As a result, agents repeat the same mistakes and discard hard-won experience. The dominant fix is \emph{bespoke memory}---retrieval, persistence, and learning logic hand-wired into one agent and bound to one storage engine. This creates a fragmented landscape where memory cannot be swapped, shared, isolated, or reasoned about independently of the agent that owns it. We argue that this is a middleware problem: agent memory deserves a first-class, pluggable layer, just as data access, mess...
| Authors | K. R. Jayaram, Vatche Isahagian, Vinod Muthusamy et al. |
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| Primary category | cs.AI |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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