MCP + A2A: The Two Protocols Building the AI Agent Internet

There are exactly two open protocols that will define how AI agents work for the next decade. The first is MCP — Model Context Protocol, created by Anthropic. The second is A2A — Agent-to-Agent Protocol, created by Google. One connects agents to tools. The other connects agents to each other. Together, they are building what I call the AI agent internet.

If you are building AI agent systems for your business — or planning to — understanding these two protocols is not optional. It is the difference between building on a foundation that lasts and building on proprietary quicksand that becomes technical debt within 18 months.

I build multi-agent systems for businesses. Every system I deploy uses MCP. Every architecture I design accounts for A2A. Here is why, and what it means for you.

MCP: How Agents Connect to Tools

Anthropic open-sourced MCP on November 25, 2024. The idea is simple but powerful: give every AI agent a universal way to connect to any tool, data source, or service.

Before MCP, connecting an AI agent to your CRM required custom API integration code. Connecting it to your email required different custom code. Connecting it to your database required yet another custom integration. Every tool needed its own connector, and none of them worked the same way.

MCP fixes this. It defines a standard protocol — like USB-C for AI — that any tool can implement once and any agent can use immediately. An MCP server exposes tools (functions the agent can call), resources (data the agent can read), and prompts (reusable instructions). An MCP client in the agent connects to these servers and uses them.

The numbers tell the story. MCP has reached 97 million monthly SDK downloads. There are over 5,800 MCP servers available. More than 300 clients support the protocol. OpenAI, Google, Microsoft, and Anthropic all support MCP. This is not an experiment — it is becoming the standard.

What this means practically: When I build an AI agent for a client and connect it to their CRM through MCP, that same connection works if the client later switches from Claude to GPT or Gemini. The tool integration is protocol-level, not model-specific. That portability is worth more than most people realize.

A2A: How Agents Talk to Each Other

Google announced A2A on April 9, 2025, at Cloud Next with more than 50 launch partners including Salesforce, SAP, ServiceNow, Atlassian, PayPal, Intuit, UiPath, Workday, LangChain, MongoDB, and Databricks.

MCP solves agent-to-tool communication. A2A solves something equally important: agent-to-agent communication. When you have multiple AI agents built by different vendors, running on different frameworks, using different models, how do they coordinate?

A2A defines a standard way for agents to discover each other, exchange tasks, stream progress updates, and return results. Here is how it works:

Agent Cards. Every A2A-compatible agent publishes a JSON metadata file (typically at /.well-known/agent.json) describing its capabilities, skills, endpoint, and authentication requirements. Think of it like a business card for AI agents — other agents can find it and know exactly what this agent can do.

Task lifecycle. One agent (the client) creates a task and sends it to another agent (the server). The task moves through states: submitted, working, input-required, completed, failed, or canceled. This supports everything from quick lookups to hours-long research processes.

Streaming and notifications. A2A supports real-time streaming through Server-Sent Events and webhook-based push notifications for long-running tasks. You can watch an agent's progress in real-time or get notified when it finishes.

The key design principle: A2A treats agents as opaque entities. You do not need to know what model powers the other agent, what framework it uses, or how it works internally. You only need to know its A2A endpoint and its capabilities. This is what makes true multi-vendor agent systems possible.

Why You Need Both

Here is the mistake most people make: they think of MCP and A2A as alternatives. They are not. They are complementary layers of the same stack.

| Protocol | What It Connects | Analogy | |---|---|---| | MCP | Agent to tools and data | USB-C — universal plug for any device | | A2A | Agent to other agents | HTTP — universal communication between servers |

A real-world example from my deployments:

Imagine a business that wants AI to handle their quarterly business review. Here is how both protocols work together:

1. The user tells their orchestrator agent to prepare the quarterly review
2. The orchestrator uses A2A to delegate to specialist agents: a CRM agent for revenue data, a marketing agent for campaign metrics, a finance agent for cost analysis
3. Each specialist agent uses MCP internally to connect to its tools — the CRM agent pulls from HubSpot via MCP, the marketing agent reads from Google Analytics via MCP, the finance agent queries QuickBooks via MCP
4. Results flow back through A2A as structured artifacts to the orchestrator
5. The orchestrator compiles everything into a coherent report

MCP gives each agent its "hands" — the ability to interact with tools. A2A gives agents the ability to collaborate — to discover each other, delegate work, and share results. You need both for a real multi-agent system.

This is not theoretical. This is how I architect every multi-agent deployment. The protocols formalize what I have been building through custom integration code. Now there is a standard way to do it.

The Enterprise Endorsement

The adoption numbers for both protocols are unprecedented for open standards:

MCP ecosystem:

• 97 million monthly SDK downloads
• 5,800+ MCP servers available
• 300+ compatible clients
• Supported by OpenAI, Google, Microsoft, Anthropic, and virtually every major AI platform

A2A ecosystem:

• 50+ launch partners on day one
• Supported by Salesforce, SAP, ServiceNow, Atlassian, PayPal, Intuit, UiPath, Workday
• Major consulting firms and system integrators on board
• Built on HTTP and JSON-RPC 2.0 — standards that every enterprise already uses

Both Google and Anthropic publicly commit to supporting each other's protocols. Google explicitly stated that A2A was designed "with MCP in mind." The two companies are not competing on protocols — they are building complementary layers of the same infrastructure.

This level of cross-company alignment in the AI industry is rare. When Google, Anthropic, Salesforce, SAP, and ServiceNow all agree on the same standards, the industry is not debating whether to adopt them. The debate is how fast.

What This Means for Your Business

Build on Open Protocols Now

If you are deploying AI agents — or evaluating whether to — the protocol question is settled. MCP for tool integration. A2A for agent coordination. Every major platform supports them. Every enterprise vendor is building for them. Proprietary agent platforms that do not support these protocols are accumulating technical debt with every deployment.

I have been building with MCP since early 2026 and designing for A2A compatibility since the protocol launched. Every agent system I deploy for clients uses MCP for tool connections. When clients ask me to add a new tool integration, it takes hours instead of weeks because MCP standardizes the interface.

Demand Protocol Support from Your Vendors

When evaluating AI tools, agent platforms, or consulting partners, ask two questions:

1. "Does your system support MCP for tool integration?"
2. "Can your agents participate in A2A for multi-agent coordination?"

If the answer to both is no, you are buying into a proprietary system that will become increasingly isolated as the rest of the industry converges on open standards.

Think in Terms of Agent Architecture

The convergence of MCP and A2A means multi-agent systems are becoming the default architecture — not a niche pattern for advanced deployments. Atlassian's Agents in Jira uses both protocols. Anthropic's Claude Cowork builds on MCP. Google's entire agent strategy is built around A2A.

If you are still thinking about AI as a single chatbot or a single automation, you are thinking one generation behind. The future is specialized agents — each connected to their tools via MCP, coordinating with each other via A2A, and orchestrated by an AI manager that delegates and tracks work.

This is exactly what my 18-agent system does. And now the industry's two most important protocols are formalizing the architecture pattern I have been deploying since January 2026.

The Bottom Line

MCP and A2A are to AI agents what HTTP and TCP/IP were to the internet. They are the foundational protocols that everything else will be built on. The companies that adopt them early — both as users and as builders — will have systems that are interoperable, portable, and future-proof.

The companies that ignore them will build systems that work today and become legacy tomorrow.

If you want help building an AI agent system on open protocols — one that connects to your tools via MCP, coordinates across functions via A2A, and works regardless of which model provider you choose — book a discovery call. I have been building on these protocols since before most companies heard of them.