AI Agent Collaboration: Protocols for Seamless Interaction

The paper proposes four protocols to enhance AI agent collaboration by enabling them to discover, exchange, and collaborate, irrespective of their underlying architectures, addressing the current limitations of AI agents operating in silos with poor tool sharing and coordination capabilities.
Model Context Protocol (MCP): Functions as a "universal adapter" for tools and APIs, utilizing JSON-RPC for standardized and secure data access, exemplified by a coding agent accessing live API documentation on demand.
Agent Communication Protocol (ACP): Facilitates rich messaging, including text, files, and streams, among agents using REST-native architecture for cross-web system compatibility, demonstrated by a support agent sending a screen recording to a billing bot.
Agent-to-Agent Protocol (A2A): Enables task delegation through "Agent Cards," which serve as digital skill badges, allowing agents to discover and "hire" each other; for example, a research bot outsourcing number crunching to a math agent.
Agent Network Protocol (ANP): Establishes decentralized marketplaces for AI agents, employing blockchain-like identities (DIDs) for verification, supporting secure teaming up of freelance AI agents via smart credentials.
The proposed implementation roadmap involves a phased approach: first, embedding tools securely using MCP; second, enabling rich data sharing with ACP; third, scaling with dynamic AI teams via A2A; and finally, expanding through open networks using ANP, envisioning an AI app store-like ecosystem.

Source:

https://www.linkedin.com/posts/suwoongsik_ai-activity-7326836754936668161-RKKR