Advancing Agentic AI through Communication Protocols - Aniket P. Kakde; Karan M. Bhoyar; Muhammad Aiman Shad; Prof. Sudesh A. Bachwani (International Journal of Scientific Research in Science and Technology, 2025)
Autonomous agents powered by Large Language Models (LLMs) require reliable and standardized frameworks to connect tools, exchange contextual information, and synchronize tasks across diverse systems. Despite growing interest in such agents, current integration with external tools remains disjointed. Developers often have to manually create interfaces, handle authentication protocols, and navigate incompatible function-calling standards across platforms. To overcome these limitations and promote the evolution of agentic AI, it is critical to establish standardized communication protocols that ensure interoperability—enabling agents and systems to seamlessly discover each other’s capabilities, share data, and coordinate operations. This paper explores a structured overview of emerging communication standards for agents, focusing on the Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP). MCP utilizes a JSON-RPC based client-server architecture to enable secure execution of tools and well-typed data transfer. ACP introduces a REST-compliant message structure with support for asynchronous streaming and multipart formats, facilitating rich, multimodal agent outputs.A2A enables agents to delegate tasks peer-to-peer using capability-rich Agent Cards, enabling scalable and distributed workflows across organizations. ANP facilitates agent discovery and secure collaboration in open networks, leveraging decentralized identifiers (DIDs) and semantic graphs based on JSON-LD.
Keywords: Large Language Models (LLMs); Agent Communication; Interoperability Protocols; Model Context Protocol (MCP); Agent Communication Protocol (ACP); Agent-to-Agent Protocol (A2A); Agent Network Protocol (ANP); Autonomous Agents; Multimodal Messaging; Decentralized Identity (DID); Agentic AI