arXiv:2510.17149v3 Announce Type: replace Abstract: As large-scale multi-agent systems evolve, the communication protocol layer has become a critical yet under-evaluated factor shaping performance and reliability. Despite the existence of diverse protocols (A2A, ACP, ANP, Agora, etc.), selection is often intuition-driven and lacks standardized guidance. We introduce ProtocolBench, a benchmark that systematically compares agent protocols along four measurable axes: task success, end-to-end latency, message or byte overhead, and robustness under failures. On ProtocolBench, protocol choice signif
The rapid development and deployment of LLM-powered multi-agent systems necessitate standardized evaluation, making the timing for ProtocolBench critical as these systems scale.
The selection of multi-agent communication protocols directly impacts the performance, reliability, and scalability of AI agent systems, which are increasingly central to enterprise and consumer applications.
Protocol selection for multi-agent systems will evolve from intuition-driven to evidence-based due to systematic benchmarking, potentially leading to more robust and efficient AI deployments.
- · AI agent developers
- · Enterprises adopting AI agents
- · Researchers in multi-agent systems
- · Inefficient protocol designers
- · Organizations with poorly chosen agent protocols
Systematic benchmarking will drive innovation and optimization in multi-agent communication protocols.
Improved protocol selection will accelerate the deployment and improve the reliability of complex AI agent applications across various industries.
Standardized best practices for agent communication could lead to new inter-operability standards and platform layers for AI agents.
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Read at arXiv cs.AI