A2A + MCP: The Two Protocols Shaping the Agentic Web
Google's Agent-to-Agent protocol and Anthropic's Model Context Protocol are becoming the TCP/IP of AI agents. Here's how they work together.
The age of isolated AI agents is ending. Two open protocols — Google's Agent-to-Agent (A2A) and Anthropic's Model Context Protocol (MCP) — are converging to create something the industry has been missing: a common language for AI systems to discover, communicate with, and coordinate each other at scale.
The Problem: Agent Silos
Right now, most AI agents live in walled gardens. Your coding assistant can't talk to your scheduling agent. Your customer support bot can't hand off to your billing system's AI. Every integration is custom glue code — brittle, expensive, and vendor-locked.
It's remarkably similar to the early internet before HTTP standardized how servers talk to browsers. We have powerful agents, but no shared protocol for them to collaborate.
MCP: Connecting Agents to Tools
Anthropic's Model Context Protocol (MCP), released in late 2024 and now governed by the Linux Foundation, solves the agent-to-tool problem. It standardizes how an AI agent discovers and uses external tools — databases, APIs, file systems, code interpreters — via a clean JSON-RPC 2.0 interface.
Think of MCP as giving agents hands. An agent using MCP can:
- Discover what tools are available
- Understand their parameters and capabilities
- Call them with structured inputs
- Process structured outputs
MCP has seen rapid adoption. OpenAI, Google, Microsoft, and AWS all support it. It's become the de facto standard for tool integration, backed by a growing ecosystem of MCP servers for everything from GitHub to Slack to databases.
A2A: Connecting Agents to Each Other
Google's Agent2Agent protocol (A2A), announced in April 2025 with over 50 technology partners, addresses a different gap: agent-to-agent communication. While MCP lets agents use tools, A2A lets agents collaborate with other agents as peers.
The protocol introduces several key concepts:
- Agent Cards: Structured profiles that describe an agent's identity, capabilities, service endpoints, and authentication requirements — like a business card for AI systems
- Task Lifecycle: A formalized workflow where a client agent discovers remote agents, authenticates, delegates tasks, and receives results (called artifacts)
- Multi-modal Communication: Agents negotiate the right format (text, audio, video, images) based on the user's interface capabilities
- Long-Running Operations: Built-in support for async tasks with progress notifications, crucial for enterprise workflows that take minutes or hours
The flow is straightforward: a client agent receives a request, evaluates available remote agents via their Agent Cards, selects the best fit, authenticates, and delegates the task. The remote agent processes it and returns artifacts. Throughout, agents exchange context and stay synchronized.
Better Together: The Full Stack
Here's where it gets interesting. A2A and MCP aren't competitors — they're complementary layers of the same stack:
| Layer | Protocol | Function |
|---|---|---|
| Agent ↔ Agent | A2A | Discovery, delegation, collaboration |
| Agent ↔ Tool | MCP | Tool discovery, invocation, data access |
An enterprise system might use A2A to let a planning agent delegate a research task to a specialized analysis agent, which in turn uses MCP to query databases, call APIs, and generate reports. The planning agent never needs to know how the research gets done — just that it gets done.
This separation of concerns mirrors decades of networking wisdom. Just as TCP handles reliable delivery while HTTP handles content, MCP handles tool execution while A2A handles agent coordination.
Why This Matters Now
The timing isn't coincidental. Three trends are converging in early 2026:
- Agent proliferation: GitHub's Agent HQ now lets developers run Claude, Codex, and Copilot simultaneously. Enterprises are deploying specialized agents across departments. The coordination problem is exploding.
- Enterprise demand: Gartner reported a 1,445% surge in multi-agent system inquiries from Q1 2024 to Q2 2025. Companies want orchestrated agent workflows, not chatbot toys.
- Security requirements: Both protocols include authentication and authorization frameworks. A2A's Agent Cards specify security schemes. MCP integrates with existing OpenAPI auth. This isn't the Wild West — it's enterprise-grade plumbing.
What Developers Should Watch
If you're building agentic applications, these protocols are worth learning now:
- MCP is production-ready with broad ecosystem support. If your agent needs tools, use MCP.
- A2A is newer but backed by major players (Google, Salesforce, SAP, and dozens more). If you're building multi-agent systems, A2A provides the coordination layer.
- Both are open standards — no vendor lock-in, community-driven evolution.
The shift from "one agent does everything" to "specialized agents collaborate" is happening fast. The protocols are ready. The question is whether your architecture is.