Scaling LLM-Driven Multi-Agent Systems: Design Principles and Architectural Scalability Analysis

2026-07-30

AI Summary

This study addresses the absence of systematic design principles for scalable multi-agent systems. The authors propose four core design principles oriented toward scalability, formulate a reference architecture based on constrained directed workflow graphs, and introduce summarization-based communication, elastic feedback, and sequential coordination mechanisms. By evaluating configurations of varying complexity on standardized end-to-end tasks, the work formalizes workflow topology for the first time and reveals the critical influence of large language model (LLM) capability thresholds on system scalability. Experimental results demonstrate that, provided LLM capabilities meet a minimum threshold, system scaling yields improved accuracy with near-linear cost growth; however, excessive architectural complexity leads to performance degradation, and consistency challenges persist across all levels of scale.

Abstract

LLM-based multi-agent systems have the potential to enable collective intelligence and scale toward solving highly complex tasks through coordinated ensembles of specialized agents. However, despite their theoretical potential, the architectural design space remains largely non-systematized and lacks broadly established design principles. Furthermore, the scalability characteristics of such systems are only partially understood so far. This paper makes two contributions. We first distill four design principles for scalable MAS architectures from a structured analysis of prior work: simplicity, elastic feedback, sequential workflows with optional loops, and summary-based communication. We operationalize these principles in a reference architecture whose topology is formalized as a constrained directed workflow graph, and we evaluate four configurations of increasing complexity on a standardized benchmark of terminal-based system engineering tasks using two LLMs of differing capability. Our findings show that scaling yields measurable accuracy improvements with approximately linear cost growth, but only when the underlying LLM exceeds a minimum capability threshold. Performance peaks at intermediate complexity, then degrades due to timeouts and evaluation limitations. In addition, persistent consistency issues emerge as a central challenge across all scaling levels. These results provide concrete design guidance for practitioners and highlight consistency and evaluation standardization as key targets for future research.

Problem

• multi-agent systems
• scalability
• LLM-driven architectures
• design principles
• consistency

Innovation

• multi-agent systems
• large language models
• scalable architecture
• design principles
• consistency