Paper Detail

Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads

Yasmine Omri, Ziyu Gan, Zachary Broveak, Robin Geens, Zexue He, Alex Pentland, Marian Verhelst, Tsachy Weissman, Thierry Tambe

arxiv Score 20.3

Published 2026-06-04 · First seen 2026-06-05

General AI

Abstract

LLM agents are increasingly deployed on long-horizon tasks requiring sustained reasoning over extended interaction histories. Realizing this at scale requires agents to persistently store, retrieve, and update their own memory across sessions. A rich ecosystem of agent memory systems has emerged spanning flat retrieval, LLM-mediated extraction, consolidating fact stores, and agentic control flows. Yet, their system-level behavior remains uncharacterized. We present the first systems characterization of agent memory. First, we introduce a system-oriented taxonomy classifying agent memory systems along four axes. Second, we build a phase-aware profiling harness attributing cost to construction, retrieval, and generation. Third, we characterize ten representative systems across two benchmark suites, uncovering how design choices shift cost across the write and read paths. Finally, we derive 10 system recommendations covering construction scheduling, capability floors, amortization via query volume, freshness-latency tradeoffs, and fleet-scale management.

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BibTeX

@article{omri2026agent,
  title = {Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads},
  author = {Yasmine Omri and Ziyu Gan and Zachary Broveak and Robin Geens and Zexue He and Alex Pentland and Marian Verhelst and Tsachy Weissman and Thierry Tambe},
  year = {2026},
  abstract = {LLM agents are increasingly deployed on long-horizon tasks requiring sustained reasoning over extended interaction histories. Realizing this at scale requires agents to persistently store, retrieve, and update their own memory across sessions. A rich ecosystem of agent memory systems has emerged spanning flat retrieval, LLM-mediated extraction, consolidating fact stores, and agentic control flows. Yet, their system-level behavior remains uncharacterized. We present the first systems characteriza},
  url = {https://arxiv.org/abs/2606.06448},
  keywords = {cs.AI},
  eprint = {2606.06448},
  archiveprefix = {arXiv},
}

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