Understanding MCP, A2A, ANP, and ACP: The Protocols Powering AI Collaboration

June 2025

This document provides a comprehensive, non-technical explanation of four key AI protocols—Model Context Protocol (MCP), Agent-to-Agent Protocol (A2A), Agent Network Protocol (ANP), and Agent Communication Protocol (ACP)—focusing on their backgrounds, relationships, and practical use cases. These protocols are emerging standards that enable AI agents to access data, communicate, and collaborate effectively, shaping the future of AI-driven systems as of June 28, 2025. Written for a layman audience, this explanation uses simple language and analogies to make the concepts accessible.

Background of Each Protocol

Model Context Protocol (MCP)

MCP, developed by Anthropic and introduced in November 2024, is an open standard designed to connect AI models, particularly large language models (LLMs), to external data sources and tools. It addresses the challenge of AI systems being isolated from relevant information by providing a standardized way to fetch context, making AI responses more accurate and useful. Anthropic describes MCP as the “USB-C port for AI,” emphasizing its role as a universal connector (WorkOS Blog).

Analogy: MCP is like a personal assistant who hands a chef pre-measured ingredients for a specific recipe, saving the chef from sorting through an entire pantry. This ensures the AI focuses on the task without being overwhelmed by irrelevant data.

Agent-to-Agent Protocol (A2A)

A2A, introduced by Google in April 2025, is an open protocol that enables communication and collaboration between AI agents, even if they’re built by different companies or run on different platforms. It allows agents to discover each other’s capabilities, negotiate tasks, and work together securely, breaking down barriers between isolated AI systems. Supported by over 50 tech partners, including Atlassian and Salesforce, A2A is designed for enterprise-scale workflows (Koyeb Blog).

Analogy: A2A is like a group of translators at a global conference, each with a business card listing their skills. They can exchange these cards, agree on tasks, and collaborate seamlessly, even if they come from different backgrounds.

 

Agent Network Protocol (ANP)

ANP is an open-source protocol focused on decentralized communication between AI agents, aiming to create a secure, efficient network for billions of agents. It uses digital IDs (W3C Decentralized Identifiers) to ensure trust and security without a central authority, making it ideal for autonomous, distributed systems. ANP is often compared to the “HTTP of the Agentic Web,” enabling agents to interact in a peer-to-peer network (MarkTechPost Article).

Analogy: ANP is like a secure online marketplace where buyers and sellers verify each other’s identities before trading, ensuring trust without a middleman.

Agent Communication Protocol (ACP)

ACP, developed by IBM and BeeAI in early 2025, is an open standard for real-time communication between AI agents in local or edge environments, such as devices or shared networks. It focuses on fast, efficient coordination with minimal reliance on the internet, making it suitable for scenarios requiring low latency. ACP evolved from the BeeAI project and is part of the open-source ecosystem under the Linux Foundation (WorkOS Blog).

Analogy: ACP is like coworkers in the same office passing notes during a meeting, allowing quick, real-time coordination without needing to send emails or use the internet.

Relationships Between Protocols

These protocols work together to create a layered system for AI interoperability, each addressing a specific aspect of how AI agents function and communicate. Here’s how they relate, using a company analogy to illustrate:

• MCP acts as the company’s shared database or intranet, providing AI agents with access to the data and tools they need to perform tasks. For example, an AI might use MCP to retrieve customer data from a CRM system.
• A2A is like the company’s email or collaboration tools, enabling AI agents from different departments (or vendors) to communicate and coordinate across platforms. For instance, A2A allows flight-booking AI to sync with a hotel-booking AI.
• ANP is like a secure, decentralized network where departments interact directly without a central server, ideal for sensitive or distributed operations. ANP could enable agents in a research network to share data securely.
• ACP is like a local area network (LAN) in one office, allowing AI agents in the same system to communicate quickly and efficiently, such as in a smart home or factory.

Together, they form a complementary ecosystem:

• MCP provides the foundation by connecting AI to external resources.
• A2A, ANP, and ACP handle communication, depending on the scenario:
• A2A for cross-platform collaboration over the internet.
• ANP for decentralized, secure networks without a central authority.
• ACP for real-time, local coordination.

This layered approach ensures AI agents can operate in diverse environments, from local devices to global networks, as detailed in MarkTechPost Article.

Use Cases and Practical Applications

To illustrate how these protocols are used, here’s a table summarizing their primary functions and example scenarios:

Protocol	Primary Function	Example Use Case
MCP	Connecting AI to external data/tools	An AI bot accessing your email to schedule meetings based on your availability.
A2A	Cross-platform agent collaboration	A travel app where one AI books flights and another books hotels, syncing dates.
ANP	Decentralized agent communication	A network of AI agents in a blockchain system sharing data securely without a server.
ACP	Local, real-time agent coordination	A smart car where AI systems for navigation and safety communicate instantly.

Detailed Use Cases

• MCP: Imagine an AI-powered customer service bot for an online store. When you ask about a recent order, MCP allows the bot to quickly access your purchase history from the store’s database, ensuring it provides accurate details without delay. This is similar to how MCP enables AI to connect with tools like GitHub or Notion for real-time data access (WorkOS Blog).
• A2A: In a travel planning app, one AI agent might handle flight bookings, another manages hotel reservations, and a third suggests local activities. A2A ensures they communicate to align your travel dates, budget, and preferences, creating a seamless itinerary. This cross-platform coordination is key for enterprise applications (Koyeb Blog).
• ANP: Consider a research lab where multiple AI agents analyze data for a scientific project. ANP allows these agents to share their findings securely in a decentralized network, ensuring privacy and autonomy without relying on a central server. This is ideal for blockchain-based systems or agent marketplaces (MarkTechPost Article).
• ACP: In a smart home, AI agents control devices like lights, security cameras, and thermostats. ACP enables them to communicate instantly adjust settings based on your actions, such as dimming lights when you leave a room. This real-time, local coordination is perfect for edge computing scenarios (WorkOS Blog).

Why These Protocols Matter

These protocols are like the “glue” that holds the AI world together, ensuring AI agents can access data, communicate, and collaborate effectively. Just as the internet relies on standards like HTTP to connect websites, AI needs protocols like MCP, A2A, ANP, and ACP to create a connected ecosystem. They are open standards, meaning anyone can use them, promoting interoperability across different AI systems and vendors. As AI becomes more integrated into daily life, these protocols are evolving to meet the needs of diverse applications, from smart homes to global research networks.

Conclusion

MCP, A2A, ANP, and ACP are essential protocols that enable AI agents to work together efficiently. MCP connects AI to data and tools, A2A facilitates cross-platform collaboration, ANP supports decentralized networks, and ACP handles local, real-time communication. Together, they form a layered system that ensures AI agents can operate in various environments, making them more powerful and versatile. As of June 28, 2025, these protocols are driving the future of AI collaboration, with ongoing adoption and development shaping their impact.