Beyond Message Passing: Toward Semantically Aligned Agent Communication

arXiv cs.AI / 4/6/2026

💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper argues that agent communication is critical infrastructure for LLM systems that coordinate with tools and other agents, especially across heterogeneous environments.
It proposes a three-layer framework—communication, syntactic, and semantic—to compare 18 representative protocols in terms of transport reliability, structured interaction, and meaning-level coordination.
The authors find a design imbalance: most protocols mature transport/streaming/schema/lifecycle features, but lack protocol-level mechanisms for clarification, context alignment, and verification.
Because semantic work is often relegated to prompts, wrappers, or application-specific orchestration, the paper highlights hidden interoperability and maintenance “technical debt.”
It offers practical guidance for selecting protocols by deployment setting and lays out a research agenda toward interoperable, secure, and semantically robust agent ecosystems beyond raw message passing.

Abstract

Agent communication protocols are becoming critical infrastructure for large language model (LLM) systems that must use tools, coordinate with other agents, and operate across heterogeneous environments. This work presents a human-inspired perspective on this emerging landscape by organizing agent communication into three layers: communication, syntactic, and semantic. Under this framework, we systematically analyze 18 representative protocols and compare how they support reliable transport, structured interaction, and meaning-level coordination. Our analysis shows a clear imbalance in current protocol design. Most protocols provide increasingly mature support for transport, streaming, schema definition, and lifecycle management, but offer limited protocol-level mechanisms for clarification, context alignment, and verification. As a result, semantic responsibilities are often pushed into prompts, wrappers, or application-specific orchestration logic, creating hidden interoperability and maintenance costs. To make this gap actionable, we further identify major forms of technical debt in today's protocol ecosystem and distill practical guidance for selecting protocols under different deployment settings. We conclude by outlining a research agenda for interoperable, secure, and semantically robust agent ecosystems that move beyond message passing toward shared understanding.