Single-agent or Multi-agent Systems? Why Not Both?

TL;DR

This paper compares multi-agent and single-agent systems in large language model applications, finding that improved LLM capabilities reduce MAS advantages and proposing a hybrid approach that enhances efficiency and accuracy.

Contribution

It provides an extensive empirical comparison of MAS and SAS, introduces mechanisms to identify error-prone agents, and proposes a hybrid paradigm to balance performance and cost.

Findings

MAS benefits diminish with advanced LLMs

Proposed mechanisms identify error-prone agents

Hybrid MAS-SAS improves accuracy and reduces costs

Abstract

Multi-agent systems (MAS) decompose complex tasks and delegate subtasks to different large language model (LLM) agents and tools. Prior studies have reported the superior accuracy performance of MAS across diverse domains, enabled by long-horizon context tracking and error correction through role-specific agents. However, the design and deployment of MAS incur higher complexity and runtime cost compared to single-agent systems (SAS). Meanwhile, frontier LLMs, such as OpenAI-o3 and Gemini-2.5-Pro, have rapidly advanced in long-context reasoning, memory retention, and tool usage, mitigating many limitations that originally motivated MAS designs. In this paper, we conduct an extensive empirical study comparing MAS and SAS across various popular agentic applications. We find that the benefits of MAS over SAS diminish as LLM capabilities improve, and we propose efficient mechanisms to pinpoint the error-prone agent in MAS. Furthermore, the performance discrepancy between MAS and SAS motivates our design of a hybrid agentic paradigm, request cascading between MAS and SAS, to improve both efficiency and capability. Our design improves accuracy by 1.1-12% while reducing deployment costs by up to 20% across various agentic applications.