arXiv:2606.19135v1 Announce Type: cross Abstract: As large language models (LLMs) advance and multi-agent systems aim to overcome the limits of standalone agents, robust communication protocols are becoming essential infrastructure for distributed agent networks. Nonetheless, the fragmented protocol landscape presents a significant interoperability challenge. This study develops a technical taxonomy to classify and analyze LLM agent communication protocols. Following an established iterative method, we defined the taxonomy's purpose, meta-characteristic, and ending conditions, then performed f
The rapid advancement of LLMs and multi-agent systems necessitates robust communication protocols to overcome interoperability challenges.
A technical taxonomy in LLM agent communication is crucial for standardizing the fragmented landscape, enabling efficient development and deployment of distributed agent networks.
The identification and classification of communication protocols will facilitate better design, interoperability, and scalability of AI agent systems.
- · AI Agent Developers
- · Multi-agent System Builders
- · Enterprises Adopting AI Agents
- · Standardization Bodies
- · Proprietary Protocol Vendors
- · Fragmented AI Agent Ecosystems
Increased interoperability between different LLM agent systems.
Accelerated development and adoption of complex multi-agent AI applications across various industries.
The potential emergence of dominant, universally accepted communication standards that define future AI agent architectures.
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Read at arXiv cs.AI