Hidden Coordination Costs in Multi-Agent AI Systems: A Comparative Study of Communication Efficiency Across LangGraph, CrewAI, and OpenAI Agents

Abstract

Artificial Intelligence systems that operate with multiple agents are increasingly being used to address workflows that are complex and distributed but the performance of multi-agent systems is often limited by the fact that the underlying communication overheads can be hidden instead of performance limits. The paper is a systematic exploration of the efficiency of inter-agent communication in three popular orchestration systems: LangGraph, CrewAI, and the OpenAI Agents SDK. A controlled benchmarking environment is crafted with the same task structure to isolate delays associated with coordination, redundancy in messages, and resource usage. The analysis shows that communication patterns have a great impact on the overall system performance, and that graph-based orchestration creates a new coordination latency, whereas sequential delegation models have a quick increase in contextual payloads. On the other hand, lightweight orchestration has lower latency but less flexibility when subject to complex workflows.The main value of the work is the introduction of a communication-focused assessment framework that measures the costs of coordination without depending on the computation of the model. Moreover, the analytically validated optimized strategies, including asynchronous execution, structured message encoding, and adaptive task scheduling are offered. The experimental results reveal that there are quantifiable improvements in the reduction of latency and the efficiency of resources with the use of these strategies. The results offer useful design tips to create scalable, high-performance multi-agent systems and form the basis of future studies on communication-aware AI orchestration.