From LLM Inference to Agentic Workloads: Characterization and Implications for Serving Systems
4.40T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2608.15127.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryA research paper introduces AgentSysBench, a benchmark suite with ten agentic applications and system-level instrumentation. It identifies six properties distinguishing agentic workloads from conventional LLM inference, including stateful execution, heterogeneous component resource affinity, shifting bottlenecks, idle state periods, control-plane overhead, and cross-request redundancy. Four design optimizations reduce latency up to 4.5x.
Why it mattersFirst systematic benchmark of agentic serving workloads. Quantifies where latency and memory actually go, and demonstrates concrete optimizations with measured gains.
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