What Is A2A Protocol? Complete Guide for 2026

The Agent2Agent (A2A) protocol is an open standard that lets AI agents talk to each other — even when they're built by different companies, on different frameworks, with different capabilities. Think of it as a universal language for AI agents.

If you're building with AI agents or evaluating tools for your business, A2A is one of two protocols you need to understand in 2026. The other is MCP (Model Context Protocol), which handles how agents connect to tools and data. A2A handles how agents connect to each other.

This guide covers everything: what A2A is, who built it, how it works, and what it means for you.

Who's Behind A2A?

Google introduced the A2A protocol in April 2025 with backing from over 50 technology partners including Salesforce, SAP, ServiceNow, Atlassian, PayPal, Intuit, and Workday. Major consulting firms like Accenture, Deloitte, McKinsey, and PwC signed on as service partners.

In June 2025, Google donated A2A to the Linux Foundation, making it a vendor-neutral open standard. IBM's BeeAI team, which had developed the similar Agent Communication Protocol (ACP), merged their efforts into A2A under the Linux Foundation umbrella. By early 2026, DeepLearning.AI launched a dedicated A2A course, and Huawei announced an A2A-T (A2A for Telecom) variant at MWC 2026.

This isn't one company's pet project. It's an industry-wide standard with real adoption.

What Problem Does A2A Solve?

Right now, most AI agents live in silos. Your Salesforce agent can't coordinate with your ServiceNow agent. Your LangChain-built agent can't collaborate with one built on Google ADK. Each framework has its own way of doing things.

A2A breaks down those walls. It gives agents a standardized way to:

• Discover each other — Agents publish "Agent Cards" (JSON files) describing what they can do, what data types they accept, and how to authenticate with them.
• Communicate securely — A2A uses HTTP, Server-Sent Events (SSE), and JSON-RPC — standards your IT team already knows.
• Coordinate on tasks — One agent can delegate work to another, get real-time status updates, and receive results back.
• Work across modalities — A2A supports text, files, audio, and video streaming. It's not limited to text-only exchanges.

How A2A Actually Works

A2A uses a client-server model with a few key building blocks:

Agent Cards are the discovery mechanism. Every A2A-compliant agent publishes a JSON file at a known URL that describes its capabilities, supported input/output types, authentication requirements, and service endpoint. Other agents read these cards to figure out who can help with what. Tasks are the unit of work. When one agent needs help, it creates a task and sends it to a remote agent. Tasks can be quick (instant response) or long-running (hours or days). A2A provides real-time status updates throughout. Messages carry the actual content between agents — instructions, context, questions, results. Artifacts are the outputs. When a remote agent finishes work, it returns artifacts — documents, data, files, or structured responses.

The whole system runs on HTTP with JSON-RPC, which means it plugs into existing enterprise infrastructure without exotic dependencies.

A2A vs MCP: Two Protocols, Two Jobs

The most common question: how does A2A relate to MCP?

Google was explicit when they launched A2A: it complements Anthropic's Model Context Protocol, not competes with it.

• MCP handles the vertical connection — how a single agent connects to tools, databases, APIs, and data sources. Tools like Anthropic MCP, MCP Server GitHub, and MCP Server SQLite use this protocol.
• A2A handles the horizontal connection — how multiple agents communicate and collaborate with each other across organizational and framework boundaries.

In practice, you'll use both. An agent uses MCP to access its tools and data, then uses A2A to collaborate with other agents that have their own tools and data.

For a deeper comparison, read our guide on A2A vs MCP: What's the Difference?.

Five Design Principles

Google designed A2A around five principles that matter for builders:

1. Embrace agentic capabilities — Agents collaborate as peers, not as tools. A2A doesn't reduce an agent to a function call; it lets agents negotiate, share context, and coordinate complex work.

1. Build on existing standards — HTTP, SSE, JSON-RPC. No proprietary transport layers. Your existing infrastructure works.

1. Secure by default — Enterprise-grade authentication and authorization, compatible with OpenAPI authentication schemes.

1. Support long-running tasks — Some work takes hours or days. A2A handles real-time status updates, notifications, and state management throughout.

1. Modality agnostic — Text, audio, video, files. A2A doesn't limit what agents can exchange.

Real-World Use Cases

Here's where A2A gets practical:

Enterprise workflow automation: A customer service agent detects a billing issue, delegates it to a finance agent built on a completely different platform, which resolves it and reports back. No human routing required. Supply chain coordination: An inventory agent (monitoring stock via MCP connections to your database) detects low stock and uses A2A to communicate with a supplier's ordering agent. Orders placed automatically. Multi-vendor agent ecosystems: Your company uses CrewAI for internal agents and a partner uses AutoGen. With A2A, these agents coordinate across organizational boundaries without either side rebuilding. Cross-platform AI stacks: Your Google ADK agents work alongside OpenAI Agents SDK-based agents and LangGraph workflows. A2A is the bridge.

What This Means for You

If you're evaluating AI agent tools or building agent workflows:

• Check A2A compatibility when choosing agent frameworks. Tools that support A2A will be more future-proof as multi-agent systems become standard.
• Use A2A and MCP together. They're complementary. MCP for tool access, A2A for agent collaboration.
• Start with Agent Cards. Even if you're not building multi-agent systems yet, publishing Agent Cards for your agents makes them discoverable and composable later.
• Don't wait for perfect tooling. The A2A GitHub repo has samples for Google ADK, LangGraph, and CrewAI. You can start experimenting today.

The official A2A documentation lives at a2a-protocol.org, and the open-source project is on GitHub.

The Bottom Line

A2A is the missing piece for multi-agent AI systems. MCP gave agents a way to use tools. A2A gives them a way to work together. As AI agents become central to how businesses operate, the ability for those agents to collaborate across vendors, frameworks, and organizations will separate the systems that scale from the ones that stay stuck in silos.

Explore our MCP hub to understand the other half of the protocol picture, or browse our tools directory to find A2A-compatible agent frameworks.