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

Researchers present the first comprehensive systems characterization of LLM agent memory architectures, introducing a taxonomy and profiling framework to analyze how different design choices impact performance across write and read paths. The study benchmarks ten representative systems and derives actionable recommendations for optimizing agent memory at scale.

LLM agents operating on long-horizon tasks face a critical infrastructure challenge: managing persistent memory across extended interactions and multiple sessions. This research addresses a gap in the field by moving beyond theoretical agent design to examine the system-level performance implications of memory architectures in production environments. The characterization reveals that memory systems occupy a design space spanning flat retrieval, LLM-mediated extraction, consolidated fact stores, and agentic control flows—each with distinct cost profiles.

The emergence of sophisticated agent frameworks has outpaced understanding of their operational characteristics. Developers building agent systems typically lack concrete data about performance tradeoffs between construction overhead, retrieval latency, and generation quality. This paper bridges that gap through phase-aware profiling that disaggregates costs across distinct operational stages.

For the AI infrastructure industry, this work has immediate practical value. The ten system recommendations—covering construction scheduling, capability floors, amortization strategies, and fleet-scale management—provide engineers with evidence-based guidance for architecture decisions. The freshness-latency tradeoff analysis particularly addresses a tension many teams face when designing retrieval systems for reasoning tasks.

Looking ahead, the standardized taxonomy and benchmarking methodology enable comparative evaluation as memory system designs evolve. This characterization likely influences how companies architect retrieval-augmented generation pipelines and agentic frameworks. The phase-aware profiling approach could become standard for evaluating new memory system proposals, similar to how computer architecture papers establish benchmarking conventions. Future work will likely extend this analysis to multi-agent scenarios and more complex reasoning patterns.

• →Agent memory systems exhibit significant cost variance across construction, retrieval, and generation phases depending on architectural choices.
• →The research establishes the first systematic taxonomy for classifying agent memory systems across four independent design dimensions.
• →Freshness-latency tradeoffs emerge as a critical design consideration for production-scale agent deployments.
• →Cost amortization via query volume should inform memory system selection for different workload intensities.
• →Phase-aware profiling methodology enables comparative evaluation across diverse memory system implementations.