AI Agent Communication from Internet Architecture Perspective: Challenges and Opportunities

2025-09-02

AI Summary

The rapid emergence of AI agent communication frameworks has exacerbated ecosystem fragmentation, leading to poor interoperability, redundant development, and heightened security risks. To address this, we adopt an Internet architecture perspective and, for the first time, systematically transfer principles and lessons from distributed systems and protocol evolution to AI agent communication. We propose a five-dimensional evaluation model—spanning scalability, security, real-time performance, high throughput, and manageability—that establishes a theoretical bridge between AI communication and Internet architecture. This model identifies critical bottlenecks and optimization pathways in multi-agent coordination. Our contributions include a rigorously grounded analytical framework and actionable design guidelines for building large-scale, cross-domain compatible, and sustainably evolvable AI agent ecosystems—validated through systematic analysis and architectural reasoning.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The rapid development of AI agents leads to a surge in communication demands. Alongside this rise, a variety of frameworks and protocols emerge. While these efforts demonstrate the vitality of the field, they also highlight increasing fragmentation, with redundant innovation and siloed designs hindering cross-domain interoperability. These challenges underscore the need for a systematic perspective to guide the development of scalable, secure, and sustainable AI agent ecosystems. To address this need, this paper provides the first systematic analysis of AI agent communication from the standpoint of Internet architecture-the most successful global-scale distributed system in history. Specifically, we distill decades of Internet evolution into five key elements that are directly relevant to agent communication: scalability, security, real-time performance, high performance, and manageability. We then use these elements to examine both the opportunities and the bottlenecks in developing robust multi-agent ecosystems. Overall, this paper bridges Internet architecture and AI agent communication for the first time, providing a new lens for guiding the sustainable growth of AI agent communication ecosystems.

Problem

• Addressing fragmentation in AI agent communication protocols
• Ensuring cross-domain interoperability for multi-agent systems
• Providing scalable and secure architecture for agent ecosystems

Innovation

• Systematic analysis from Internet architecture perspective
• Distilling five key Internet evolution elements
• Bridging Internet architecture with AI communication