Orchestrated multi agents sustain accuracy under clinical-scale workloads compared to a single agent

Abstract

We tested state-of-the-art large language models (LLMs) in two configurations for clinical-scale workloads: a single agent handling heterogeneous tasks versus an orchestrated multi-agent system assigning each task to a dedicated worker. Across retrieval, extraction, and dosing calculations, we varied batch sizes from 5 to 80 to simulate clinical traffic. Multi-agent runs maintained high accuracy under load (pooled accuracy 90.6% at 5 tasks, 65.3% at 80) while single-agent accuracy fell sharply (73.1% to 16.6%), with significant differences beyond 10 tasks (FDR-adjusted p < 0.01). Multi-agent execution reduced token usage up to 65-fold and limited latency growth compared with single-agent runs. The design's isolation of tasks prevented context interference and preserved performance across four diverse LLM checkpoints. This is the first evaluation of LLM agent architectures under sustained, mixed-task clinical workloads, showing that lightweight orchestration can deliver accuracy, efficiency, and auditability at operational scale.

Figure 1.

Overview of the pipeline design.

Figure 2.

Accuracy and token usage across: above- GPT-4.1 mini model, while below represents the pooled data across the 4 models.