Google's A2A Protocol Explained: What Is the Difference Between A2A and MCP?

AI agents are moving from isolated demos into systems that can search, use tools, exchange information and hand work to other agents. That shift creates a basic infrastructure problem: how does an agent built by one company collaborate with an agent built by another, and how does either agent safely connect to the data and tools it needs?

Two open standards are becoming central to the answer. Model Context Protocol, or MCP, connects AI applications with tools, data sources and workflows. Agent2Agent, or A2A, provides a common way for independent agents to discover one another, delegate tasks and exchange results across vendor and framework boundaries.

The two protocols are often presented as competitors. That is the wrong mental model. MCP is mainly about an agent reaching capabilities and context. A2A is mainly about one agent working with another agent. A production system may use both at the same time.

The distinction became more important on August 17, 2026, when A2A joined the Agentic AI Foundation as a hosted project. That move places A2A in the same neutral open-agent ecosystem as MCP and other infrastructure projects, giving businesses a clearer path toward multi-vendor systems.

This guide explains A2A vs MCP without assuming deep protocol knowledge, then shows where each standard fits in a real workflow.

Why AI Agents Need Shared Protocols

An AI model can generate text with no connection to the outside world. An agent goes further: it can plan steps, call tools, read files, query systems and act toward a goal. Our guide to what AI agents can actually do in 2026 covers that shift from answering questions to executing work.

Execution creates integration problems. Imagine a company with a sales agent in one platform, a procurement agent in another and a support agent built with an internal framework. Without shared standards, developers may need custom connectors for every pair of systems. Each new vendor adds more code, security review and maintenance.

Open protocols reduce that duplication. They define common messages, discovery methods and security expectations so developers do not need to invent the same integration repeatedly.

The web became useful at global scale because browsers, servers and networks agreed on shared standards. Agent infrastructure is now searching for a similar foundation.

What Is A2A?

A2A stands for Agent2Agent. Google announced the protocol in April 2025 with support from more than 50 technology partners. The original Google announcement described it as an open protocol for agents to communicate, securely exchange information and coordinate work across applications and enterprise platforms.

In A2A, an agent can publish an Agent Card: a structured description of its identity, capabilities, supported communication methods and how another agent can reach it. A client agent can read the card, determine whether the remote agent is suitable and delegate a task.

The remote agent does not need to expose its private memory, prompts or internal tools. It behaves as an independent service that accepts work, reports progress and returns results or artifacts.

This boundary is useful in multi-company or multi-platform environments. A travel-planning agent could ask an airline agent to find eligible flights without learning how the airline's internal systems work. A purchasing agent could hand a compliance check to a specialist agent and receive a structured result.

Core A2A capabilities

A2A is designed around several practical needs:

Capability discovery through Agent Cards.

Task delegation between client and remote agents.

Progress updates for work that may take minutes, hours or longer.

Exchange of messages and output artifacts.

Authentication and authorization suitable for enterprise systems.

Support for multiple modalities, including more than plain text.

The protocol uses familiar web technologies such as HTTP and JSON-based messaging. That lowers the barrier for teams already operating APIs and service infrastructure.

What Changed in August 2026?

The Agentic AI Foundation announced that A2A would join AAIF as a hosted project. The foundation says A2A is backed by more than 150 organizations and already runs in production across areas such as mobile platforms, cloud infrastructure, financial services and supply chains.

A2A v1.0 had arrived in March 2026 with features including multi-protocol bindings, version negotiation, multi-tenancy and signed Agent Cards. Moving the project into AAIF gives the protocol a neutral governance structure where competitors can contribute without one vendor controlling the roadmap.

Neutral governance does not guarantee adoption. Developers still need good SDKs, secure implementations and business reasons to connect agents. But it reduces the fear that a foundational standard will become locked to one cloud or product.

What Is MCP?

MCP stands for Model Context Protocol. It is an open standard for connecting AI applications to external systems. An MCP-enabled application can discover and use resources such as files, databases, search systems, calendars, APIs and specialized prompts through a consistent interface.

The common analogy is a USB-C port for AI. Instead of building a different custom connection for every model and tool, developers can implement an MCP server that exposes capabilities in a standard way.

According to the official MCP architecture documentation, MCP uses a host-client-server model. The host is the AI application. It creates an MCP client for each server. The server provides context or capabilities such as resources and tools.

An MCP server might expose a read-only company knowledge base, a database query function or a calendar action. The AI application decides when to request the capability and how to use the result.

MCP's main building blocks

MCP generally helps an AI application work with:

Resources that provide data or context.

Tools that perform actions or computations.

Prompts and workflow templates.

Notifications and other structured protocol features.

MCP does not define how the model should reason or how an AI product should manage every part of the conversation. It standardizes the connection layer.

A2A vs MCP: The Simple Difference

The easiest way to remember the distinction is this:

MCP connects an AI application to tools and data. A2A connects an AI agent to another AI agent.

Suppose a business has a customer-service agent. It may use MCP to retrieve an order from a database and call a refund tool. If the case requires specialist fraud analysis, it may use A2A to delegate that investigation to a separate risk agent.

The risk agent may then use its own MCP connections to access approved fraud data and analysis tools. A2A carries the work between agents; MCP gives each agent access to its capabilities.

A useful analogy

Think of a company office. MCP is the standardized access system that lets an employee use the printer, filing cabinet, database and expense tool. A2A is the communication and delegation system that lets one employee ask a specialist in another department to complete part of a project.

Both are necessary in a complex organization. Access to tools does not replace collaboration, and collaboration does not remove the need for tools.

Are A2A and MCP Competitors?

Not in their primary roles. Google described A2A as complementary to MCP from the beginning. The protocols overlap around some practical concerns-identity, transport, security and long-running work-but they solve different integration problems.

A developer can sometimes expose a simple specialist function as an MCP tool instead of building a full remote agent. That may be the right choice when the capability is narrow and predictable. A2A becomes more useful when the remote system has its own goals, state, workflow, expertise or need to negotiate how work is performed.

The choice is therefore not based on marketing. It is based on the boundary of responsibility.

When Should Developers Use MCP?

Use MCP when an AI application needs a standardized connection to data or an action. Typical examples include reading files, searching documentation, querying a database, creating a calendar event or calling an internal business API.

MCP is also a natural fit when the host application should remain in control of the user experience and reasoning flow. The server exposes a capability; it does not need to behave like an independent collaborator.

The July 2026 MCP specification made the protocol more suitable for large deployments by introducing a stateless core, header-based routing, cacheable lists, authorization improvements and an extensions framework. The 2026-07-28 release notes show how quickly the standard is maturing beyond local developer experiments.

When Should Developers Use A2A?

Use A2A when independent agents need to discover one another, delegate work and exchange progress or results. It is especially relevant when the agents use different frameworks, belong to different departments or run in different companies and clouds.

A2A can support tasks that do not finish in a single API call. A research agent may take time to gather evidence. A logistics agent may wait for another system. A human may need to approve a step. The protocol is designed to communicate state and results across that longer lifecycle.

The remote agent remains opaque. The client does not need direct access to its internal tools or reasoning. This separation can make ownership clearer, though it also requires strong identity, authorization and audit controls.

A Realistic Workflow Using Both

Consider a small business that wants an AI system to plan and approve a marketing campaign.

A campaign agent receives the goal, budget and audience.

Through MCP, it reads brand guidelines and past campaign data.

Through A2A, it asks a research agent to analyze current market signals.

The research agent uses its own MCP tools to search approved sources and query analytics.

The result returns to the campaign agent as a structured artifact.

The campaign agent uses MCP to create a draft in the content system.

A human reviews the plan before any paid campaign is launched.

This example shows why model choice is only one layer. A business might use different models for the campaign and research agents. Our GPT-5.6 vs Gemini 3.7 Flash vs Claude Opus 5 comparison can help evaluate model strengths, but interoperability and governance decide whether the overall system works reliably.

Why Neutral Governance Matters

The A2A move gives the Agentic AI Foundation a broader stack of open projects covering instructions, runtime, tool connections and agent communication. When standards sit under neutral governance, vendors can compete on products while sharing the infrastructure needed for compatibility.

This can reduce lock-in. A business may be able to combine a fast, low-cost model for routine work with a more capable model for complex tasks. Developments such as Gemini 3.7 Flash, DeepSeek V4-Flash and GPT-5.6 Ultrafast make that flexibility increasingly valuable.

However, an open protocol is not automatically a secure protocol implementation. Teams still need to control credentials, validate Agent Cards, restrict tool permissions, log actions and require human approval for high-impact changes.

Security Risks Developers Should Not Ignore

Agent interoperability expands the attack surface. A malicious remote agent could misrepresent its capabilities, return poisoned data or attempt to manipulate the client agent. An over-permissioned MCP tool could expose files or execute an action that the user did not intend.

Developers should use least-privilege access, explicit authorization, signed identities, input validation and detailed audit logs. High-risk actions-sending money, deleting data, publishing content or changing access-should require confirmation outside the model's own reasoning loop.

Prompt injection also remains a serious concern. Content retrieved through a tool or another agent may contain instructions designed to hijack the workflow. Systems should treat external content as untrusted data rather than as authoritative instructions.

What A2A and MCP Mean for the Future of AI

AI products are likely to become less monolithic. Instead of one assistant attempting every task, a coordinator may work with specialized agents and standardized tools. Users may not see the protocols, just as most people do not think about HTTP when opening a website.

The winning systems will not necessarily use the single highest-scoring model. They will combine appropriate models, useful context, reliable tools, secure delegation and human oversight. Protocols provide the connective tissue, not the intelligence or judgment by themselves.

Frequently Asked Questions

Is A2A owned by Google?

Google created and launched A2A, then donated it to Linux Foundation governance. In August 2026 it joined the Agentic AI Foundation as a hosted project.

Who created MCP?

Anthropic introduced MCP as an open standard for connecting AI applications with data and tools. It is now part of the broader open agent ecosystem under neutral governance.

Can A2A replace MCP?

Usually no. A2A handles communication and delegation between agents, while MCP connects AI applications with tools and context. A system may use both.

Can MCP connect two agents?

A developer can expose a narrow capability through an MCP tool, but a fully independent agent with its own workflow and task lifecycle is closer to the problem A2A is designed to solve.

Do small developers need A2A now?

Not every application needs multi-agent interoperability. A simple chatbot with a few tools may only need MCP or direct APIs. A2A becomes relevant when independent agents need to collaborate across clear system boundaries.

Final Thoughts

The A2A vs MCP debate becomes simple once the boundary is clear. MCP gives an AI application a standard way to reach tools and data. A2A gives independent agents a standard way to find one another, delegate work and exchange results.

The two protocols are building different layers of the same future. Their shared home under open governance could make it easier for businesses to mix vendors and frameworks without rebuilding every connection. But interoperability must grow alongside security. The goal is not to let every agent talk to everything; it is to let approved agents perform approved work through observable, controlled connections.