Benchmarking Inference Engines on Agentic Workloads

ResearchBenchmarking Inference Engines on Agentic WorkloadsApr 22, 2026Oam Patel, Linden LiLarge language model inference engines are typically benchmarked with prompt-heavy, decode-heavy, or balanced workloads. InferenceX from SemiAnalysis, for example, tests a workload with a fixed number of input and output tokens (e.g. 1,000 tokens in, 8,000 tokens out). Before the advent of agents that could aggressively call tools, most workloads were simple: chatbots that would think while answering a math problem, API calls that would summarize a long body of text, or coding autocomplete that would take in the current file and emit a short suggestion.Agentic applications today have a very different shape: multi-turn, tool-using workloads that have produced a surge in the demand for inference…

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