The AI Protocol Wars Have Begun
Google, Anthropic, and the Linux Foundation are racing to control how AI agents communicate. Here's what leaders must know.
Agentic AI will soon power autonomous systems that think, plan, negotiate, and act, without constant human prompts.
Four emerging protocols — MCP, A2A, ACP, and ANP — aim to shape the next decade of AI interoperability.
Understanding them now helps leaders build flexible, future-proof AI strategies with less risk and more upside.
Beyond the Chatbot: AI Is Becoming Autonomous
We’re entering a new phase in artificial intelligence. Tools like ChatGPT and Claude are evolving into autonomous agents: digital workers that operate with goals, tools, and the ability to collaborate.
These agents don’t just respond to prompts — they make decisions, coordinate with others, and act on your behalf. However, just as the early internet required protocols like TCP/IP to connect the world's computers, autonomous AI requires a new architecture to scale and prevent chaos.
That’s where agentic AI standards come in.
They define how agents connect to data, talk to each other, and discover new capabilities. These aren’t just technologies — they’re the frameworks for how your future digital workforce will operate.
Meet the Architectures
Four major protocols are being proposed to create this "internet of agents":
MCP (Model Context Protocol) – from Anthropic
A2A (Agent2Agent Protocol) – from Google & partners
ACP (Agent Communication Protocol) – from the Linux Foundation
ANP (Agent Network Protocol) – from the Open Source community
To understand how they work — and why it matters — let’s walk through a relatable task:
“Help me plan and order groceries for the week. I’ve got $150 to spend, need nut-free snacks for my kid, and want three dinner recipes.”
How Each Architecture Handles It
MCP: The Universal Data Plug
MCP provides a standard, secure way for an agent to connect to any tool or database. It’s the universal plug for data.
It lets your agent securely ask the grocery store’s database: “What nut-free snacks are in stock?”
It connects to a recipe API to pull three dinner ideas that match your preferences.
It ensures all data is exchanged in a predictable, secure format, replacing brittle, custom integrations.
Analogy: The USB-C port for AI — a single, reliable way to connect your agent to any tool.
Pros: It directly solves the urgent problem of connecting agents to tools securely. Backing from a major player like Anthropic gives it immediate credibility and a ready-made ecosystem. Its focus on a single job — the data connection — makes it relatively simple to implement.
Cons: Its success hinges entirely on widespread adoption. If it only becomes the standard for Anthropic's partners, it risks becoming a niche protocol rather than a universal one. It doesn't solve the challenge of how agents collaborate with each other.
A2A: The Collaborative Playbook
A2A proposes a protocol for a team of agents to coordinate on a complex task. It’s the playbook for collaboration.
A “Meal Planner Agent” builds the menus and adds ingredients to a shared shopping list.
A “Nutrition Agent” reviews the list to ensure all snack choices are nut-free.
A “Finance Agent” monitors the running total to keep the order under the $150 budget.
Analogy: Like a shared project plan in Asana or a Trello board, where a team of experts coordinates their actions.
Pros: It has massive industry momentum, backed by Google and a powerful coalition of over 50 tech giants. It is designed specifically for the complex, multi-step business workflows that hold the most value. It provides a structured playbook for agent teamwork.
Cons: With so many partners, the protocol risks becoming overly complex or slow to evolve due to "design by committee." There's also the strategic risk of a standard so heavily influenced by one corporate giant, which could favour Google's ecosystem in the long run.
ACP: The Universal Translator
ACP is a low-level protocol that ensures any agent can reliably communicate with any other agent, regardless of who built it. It’s the universal translator.
When your Google-built “Finance Agent” sends a budget warning...
...the message is wrapped in an ACP format.
...so your Anthropic-built “Meal Planner Agent” can receive and understand it perfectly.
Analogy: Like the underlying postal service (or TCP/IP) for AI, the invisible but essential plumbing that ensures messages are always delivered.
Pros: Its governance under the neutral Linux Foundation eliminates the risk of a single corporation controlling the standard. It is built to be a universal, foundational layer, ensuring maximum flexibility to support any other framework built on top. It’s a truly open, future-proof bet.
Cons: Lacking a major corporate backer, its adoption path is slower and relies on grassroots developer support. It is a "low-level" protocol, meaning it only ensures messages are delivered, not what they mean — requiring another framework on top to manage business logic.
ANP: The Open Marketplace
ANP envisions a decentralised protocol where agents can dynamically find and “hire” other agents on an open network.
Your agent broadcasts a job to the network: “Seeking an agent who can find the best price on organic apples.”
It discovers agents representing Whole Foods, a local farm co-op, and Instacart.
It chooses one based on price, reputation, or delivery speed and pays it to complete the task.
Analogy: Airbnb or Upwork for AI agents; a dynamic, real-time marketplace for digital services.
Pros: It offers the most powerful and visionary future — a truly open and dynamic marketplace for AI services that would drive innovation and prevent vendor lock-in entirely. It unlocks the potential for entirely new, agent-driven business models.
Cons: It is the least mature and most conceptual of the four. It faces immense unsolved challenges in security, trust, and governance. For a CIO, it represents the highest risk and is the furthest from being enterprise-ready today.
Will One Protocol Win?
Probably not, because they solve different problems at different layers. An advanced system of the future will utilise all of these: it will employ MCP to gather data, A2A to coordinate its internal team, ACP as the universal transport layer for its messages, and ANP to find and hire outside assistance. The key is that they are all pushing for an open, interoperable future.
What You Should Do Now
Ask vendors if they’re aligning with A2A, MCP, ACP or ANP. These open standards matter for long-term integration and avoiding lock-in.
Experiment with multi-agent workflows inside marketing, finance, or ops to understand the practical benefits of collaboration.
Prioritise partners who embrace openness over those building proprietary, walled-garden agent systems.
The AI landscape is shifting from models to systems — from prompts to protocols. If you want agents that can scale, cooperate, and participate in the broader digital economy, you’ll need to build on the right architectural foundations. Now is the time to invest in those foundations, before the stack hardens.