[2603.04443] AMV-L: Lifecycle-Managed Agent Memory for Tail-Latency Control in Long-Running LLM Systems

Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2603.04443 (cs) [Submitted on 22 Feb 2026] Title:AMV-L: Lifecycle-Managed Agent Memory for Tail-Latency Control in Long-Running LLM Systems Authors:Emmanuel Bamidele View a PDF of the paper titled AMV-L: Lifecycle-Managed Agent Memory for Tail-Latency Control in Long-Running LLM Systems, by Emmanuel Bamidele View PDF Abstract:Long-running LLM agents require persistent memory to preserve state across interactions, yet most deployed systems manage memory with age-based retention (e.g., TTL). While TTL bounds item lifetime, it does not bound the computational footprint of memory on the request path: as retained items accumulate, retrieval candidate sets and vector similarity scans can grow unpredictably, yielding heavy-tailed latency and unstable throughput. We present AMV-L (Adaptive Memory Value Lifecycle), a memory-management framework that treats agent memory as a managed systems resource. AMV-L assigns each memory item a continuously updated utility score and uses value-driven promotion, demotion, and eviction to maintain lifecycle tiers; retrieval is restricted to a bounded, tier-aware candidate set that decouples the request-path working set from total retained memory. We implement AMV-L in a full-stack LLM serving system and evaluate it under identical long-running workloads against two baselines: TTL and an LRU working-set policy, with fixed prompt-injection caps. Relative to TTL, AMV-L improves th...