First systems characterization of agent memory workloads published
Researchers Yasmine Omri, Ziyu Gan, and Zachary Broveak present the first systems-level characterization of agent memory, profiling ten representative memory systems across two benchmark suites and deriving 10 system recommendations.
Score breakdown
This is the first systems-level characterization of agent memory, providing a taxonomy, profiling methodology, and concrete recommendations that address a previously uncharacterized gap in deploying stateful long-horizon LLM agents at scale.
- 01Authors Yasmine Omri, Ziyu Gan, and Zachary Broveak present the first systems characterization of agent memory.
- 02A system-oriented taxonomy classifies agent memory systems along four axes.
- 03A phase-aware profiling harness attributes cost to construction, retrieval, and generation phases.
Yasmine Omri, Ziyu Gan, and Zachary Broveak present a paper that characterizes, for the first time at a systems level, the behavior of agent memory systems used in long-horizon LLM workloads. As LLM agents are increasingly deployed on tasks requiring sustained reasoning over extended interaction histories, a rich ecosystem of memory approaches has emerged — spanning flat retrieval, LLM-mediated extraction, consolidating fact stores, and agentic control flows — yet their system-level behavior has remained uncharacterized until now.
The paper makes four core contributions: a system-oriented taxonomy classifying agent memory systems along four axes; a phase-aware profiling harness that attributes computational cost to the construction, retrieval, and generation phases; a characterization of ten representative systems across two benchmark suites; and a set of 10 system recommendations. Those recommendations address construction scheduling, capability floors, amortization via query volume, freshness-latency tradeoffs, and fleet-scale management — providing concrete guidance for deploying agent memory at scale.
Key facts
- 01Authors Yasmine Omri, Ziyu Gan, and Zachary Broveak present the first systems characterization of agent memory.
- 02A system-oriented taxonomy classifies agent memory systems along four axes.
- 03A phase-aware profiling harness attributes cost to construction, retrieval, and generation phases.
- 04Ten representative memory systems are characterized across two benchmark suites.
- 05Design choices are shown to shift cost across the write and read paths.
- 06The paper derives 10 system recommendations covering construction scheduling, capability floors, amortization via query volume, freshness-latency tradeoffs, and fleet-scale management.
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