Standardizing AI Agent Protocols: Definitions, Layered Architectures, and Future Research Directions

Abstract

AI agents are moving from isolated task executors to networked, autonomous participants in software ecosystems. As a current member of the W3C AI Agent Protocol Community Group, I draw on ongoing standards discussions and emerging best practices to frame the interoperability and governance problem. As agents increasingly discover resources, invoke tools, negotiate tasks, and coordinate with humans and other agents, ad hoc interfaces fail to provide interoperability, security, and predictable behavior at scale. This article defines AI Agent Protocols as composable specifications spanning transport, message semantics, capability representation, policy enforcement, and governance. We synthesize recent surveys of agent protocols, standards efforts including the W3C AI Agent Protocol Community Group, and emerging interoperability layers such as the Model Context Protocol to propose a reference architecture and taxonomy. We analyze design tradeoffs, threat models, and conformance criteria, and identify research directions: semantic interoperability, verifiable identity, policy-carrying messages, adaptive coordination, and safety-by-construction for large-scale multi-agent systems.