Published on August 21, 2026, the paper presents a benchmark covering 10 agentic applications and reports that model inference is often not the main serving bottleneck.
The paper presents AgentSysBench, a benchmark covering 10 agentic applications. According to the summary, model inference is often not the main serving bottleneck in these workloads. The work evaluates four areas: task-aware serving, communication-aware placement, state offloading, and caching.
The results suggest that serving systems for agents may need to optimize models, tools, memory, and communication jointly, rather than focusing only on inference. To verify the scope of these conclusions, consult the original paper, including its methodology, evaluated workloads, and results for each technique; the available summary does not specify metrics or experimental conditions.