Benchmark RadarAgentSysBench
AgentSysBench is a benchmark suite and measurement toolkit for characterizing agentic workloads on LLM serving systems. It includes ten representative agentic applications and unified instrumentation, identifying six properties that distinguish agentic workloads from conventional LLM inference.
- Released
- 2026-08-15
- Readiness
- Paper only
- Primary field
- General AI
Why it matters
Serving systems are designed for conventional LLM inference and may not handle agentic workloads efficiently. AgentSysBench provides a measurement-based characterization that could guide system design for agentic applications.
Motivation
Agentic applications are shifting AI serving from isolated model inference to long-running workloads in which LLMs coordinate tools, environments, and persistent state.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.