Google has just introduced A2A (Agent2Agent), a new open protocol built for direct communication between AI agents. The announcement has sparked immediate debate, with many questioning whether this move signals the beginning of a protocol standoff with MCP (Model Context Protocol). Some see it as a clear challenge to MCP’s growing presence, raising the question: is Google drawing the first line in a new battle for agent interoperability?

Why AI Protocols Are Becoming a Battleground

AI is shifting toward interconnected ecosystems where agents aren’t just performing tasks — they’re reasoning, coordinating, and working together across systems. In this environment, the need for shared rules — standardized protocols — is becoming central to how everything fits together.

In Google’s A2A announcement, they state:

“Standard protocols are essential for enabling agentic interoperability, particularly in connecting agents to external systems.”

With A2A (Agent2Agent), Google lays out two key layers required to build functional agentic systems:

1. Tools and Data Integration — A standardized way for agents and large language models (LLMs) to access external tools, APIs, or databases.
2. Agent-to-Agent Communication — A shared protocol for how intelligent agents exchange information, delegate responsibilities, and collaborate.
   

This is where the tension begins.

MCP (Model Context Protocol) focuses on the first layer. It’s designed to organize what agents, tools, or users feed into a model — essentially structuring the inputs. A2A, on the other hand, addresses the second layer: coordination between autonomous agents themselves. This isn’t just a technical difference — it defines how responsibilities are distributed across the ecosystem.

By decoupling the agent communication layer from the tools-and-data layer, Google positions A2A as a complement to MCP rather than a direct rival. At least for now. Whether that stance holds in practice remains to be seen — and we’ll return to that point shortly.

But the stakes are much higher than naming conventions and protocol specs.

Protocols aren’t just communication rules — they shape entire ecosystems. They determine who builds the infrastructure, which tools gain traction, and how easily new systems can plug in. In a space where speed, scale, and interoperability drive adoption, protocols become the architecture of influence.

What Is Google’s A2A (Agent-to-Agent)?

As the name suggests, A2A (Agent-to-Agent) is an open protocol developed by Google to standardize communication between AI agents. It’s not just a concept or framework — it’s a technical specification meant to define how intelligent systems talk, share data, and work together.

According to Google’s official documentation and announcement, A2A is built to allow agents to:

• Communicate directly — Agents can exchange messages with one another in a structured and predictable way, without needing to route everything through a central system.
• Exchange information securely — A2A includes guidelines for secure data sharing, ensuring that agents can pass information without exposing systems to unnecessary risk.
• Coordinate actions across environments — Whether it’s scheduling across calendars, triggering workflows, or invoking APIs, A2A is built for coordination that spans tools, services, and enterprise systems.
  

In essence, A2A is Google’s proposal for how agentic systems should scale: not by stacking everything into a single monolithic LLM, but by building a network of cooperating agents, each with defined roles and communication standards.

How A2A Works: A Protocol for Multi-Agent Systems

At its core, A2A (Agent-to-Agent) is a communication protocol designed to bring order and consistency to how autonomous agents discover and interact with each other. Rather than relying on ad hoc integrations, A2A provides a structured system that defines who agents are, what they can do, and how they exchange information.

Agent Discovery via Public Cards

A2A begins with a discovery mechanism. Agents make themselves publicly discoverable by exposing a public card over HTTP. This card serves as a kind of digital business card and contains:

• Hosted/DNS Information — Specifies where the agent is accessible and how to connect to it.
• Version — Indicates which version of the agent is currently running. This is critical for maintaining compatibility across updates.
• Skills — A structured list that describes the agent’s capabilities — essentially, the tasks or services it can perform.
  

This card system allows agents to query and identify other agents dynamically, without prior hardcoding or manual configuration.

Communication Models in A2A

Once agents discover each other, A2A supports multiple modes of client-server communication depending on task duration and interaction style:

• Request/Response with Polling — The traditional HTTP pattern, where the client sends a request and checks back for results. Useful when the task completion time is uncertain.
• Server-Sent Events (SSE) — Ideal for short-running tasks, this method lets the server push real-time updates to the client over a persistent connection.
• Push Notifications — Used for long-running operations where the client doesn’t need to wait. Instead, the agent pushes a notification once the task is complete.

This flexible architecture ensures A2A can support a wide range of agent behaviors — from quick lookups to complex workflows that take minutes or hours to complete.

What Is Anthropic’s MCP (Model Context Protocol)?

MCP (Model Context Protocol) is an open standard developed by Anthropic that defines how applications can provide structured context to large language models (LLMs) and AI assistants. It’s designed to streamline the way models interact with external systems by enabling secure, two-way connections between LLMs and the tools, APIs, and data sources they rely on.

Rather than focusing on how agents talk to each other, like A2A, MCP focuses on how external information is delivered into the model’s context — and how the model’s outputs can trigger real-world actions in return.

With MCP, developers can build intelligent agents and workflows that:

• Pull data from external sources like APIs, databases, and business platforms
• Send updates to systems like project management tools, CRMs, or version control repositories
• Operate within complex workflows that require up-to-date, real-world information
  

In practice, this means agents built on top of MCP can do far more than just respond to prompts — they can reason with live data, interact with real tools, and provide tailored, situational outputs that reflect what’s happening in the broader system.

MCP lays the groundwork for making LLMs active participants in business and software ecosystems, rather than passive text generators.

How MCP Works: A Protocol for Context-Driven Agent–Tool Communication

MCP (Model Context Protocol) operates on a client-server architecture designed to connect language models with external tools and data sources in a secure, structured way. It’s not just about sending inputs to a model — it’s about building systems where LLMs can interact with their environment intelligently.

Key Components in the MCP Ecosystem

• MCP Hosts — These are applications that act as the central hub, like Claude Desktop, development environments, or custom AI assistants. They use MCP to request data or trigger actions through external tools.
• MCP Servers — These are services that expose specific capabilities — such as access to files, APIs, or databases — by implementing the MCP protocol. Think of them as the interface layer between LLMs and real-world systems.
• MCP Clients — These are typically the language model-powered applications (like chatbots or agents) that initiate communication with MCP servers to fetch context or perform actions.
• Local Data Sources — Files, databases, or software on your computer that MCP can securely access through a local MCP server.
• Remote Services — External systems accessible over the internet (usually via API), like CRM platforms, cloud storage, or analytics tools, which MCP can connect to through compatible servers.
  

How Communication Works

MCP servers expose a set of APIs and endpoints that allow MCP clients to connect, request information, and receive structured responses. This lets agents:

• Pull in relevant data at runtime
• Operate with full awareness of context from multiple systems
• Push updates or trigger actions across the connected toolset
  

Since its launch, MCP has quickly become a go-to standard for integrating LLMs with real-world applications. It bridges the gap between model reasoning and external execution — giving AI agents the context they need to operate effectively inside existing software ecosystems.

Are A2A and MCP Addressing Different Use Cases?

Google has taken deliberate steps to position A2A as a protocol that complements rather than competes with Anthropic’s MCP. In its official announcement, Google states:

“A2A is an open protocol that complements Anthropic’s MCP, which provides helpful tools and context to agents.”

The distinction, as Google frames it, lies in their target layers within the agent ecosystem:

• MCP enables tool integration. It connects agents to structured environments — APIs, databases, control systems — allowing them to take actions like “raise platform by 2 meters” or “turn wrench 4 mm to the right.”
• A2A enables communication and coordination. It focuses on how agents — or users acting through agents — interact with each other over time to diagnose problems, ask clarifying questions, or develop shared plans.
  

In the A2A documentation, there’s even a dedicated page titled “A2A ❤️ MCP”, featuring a practical example from a car repair shop:

• MCP handles low-level integration. Each repair agent is connected to tools through MCP — enabling precise mechanical actions.
• A2A manages high-level dialogue. Customers can describe symptoms (“my car is making a rattling noise”), and the repair agents can ask questions, share images, or bring in other agents like part suppliers to assist in the repair process.
  

This example illustrates Google’s central argument: MCP is for structured action, while A2A is for ongoing, multi-agent conversation and coordination.

But Is the Line Really That Clear?

While the two protocols appear to cover distinct areas, the line between them isn’t always easy to draw. Google’s argument rests on a clean separation between agents and tools — that agents are autonomous actors capable of dialogue and reasoning, while tools are passive systems invoked by commands. But in practice, this distinction often blurs.

For example:

• A complex tool wrapped in an API (like a diagnostic engine or financial model) might operate more like an agent.
• An “agent” that only calls a few scripted tools might behave more like an orchestrator or interface.
• Some AI assistants might need to both converse with peers (A2A) and access tools (MCP) within the same workflow.
  

As systems become more interconnected and layered, what counts as “inter-agent communication” vs. “tool orchestration” can start to overlap — especially when agents are managing other agents that themselves control tools.

So while Google’s division of labor between A2A and MCP makes sense in theory, the real-world implementation may not always follow clean boundaries. The protocols may begin to compete, overlap, or even converge as more developers build on top of them — especially in workflows that require both deep context and multi-agent collaboration.

A2A and MCP: Competition or Complement?

On the surface, Google is careful to frame A2A as a complement to Anthropic’s MCP — not a competitor. But beneath the language of coexistence, there’s a deeper question: Do we actually need both protocols to build multi-agent systems?

Theoretically, the two can live side by side — one for inter-agent dialogue (A2A), the other for structured tool integration (MCP). But in practice, that separation is rarely clean.

“In theory they can coexist. In practice I foresee a tug of war. Developers can only invest their energy into so many ecosystems.”
— Solomon Hykes, CEO of Dagger, ex-Docker

Hykes’ comment touches on two important realities:

1. The line between agents and tools is increasingly blurry.
   Tools are becoming smarter — capable of reasoning, making decisions, and even initiating actions. At the same time, many agents are little more than wrappers around powerful tools. As a result, it’s no longer obvious which communication belongs in the “agent” layer and which belongs in the “tool” layer.
2. Ecosystem traction determines the winner — not technical elegance.
   Developers don’t adopt protocols because they’re theoretically sound. They adopt them because the ecosystem has momentum: partners, SDKs, community support, real-world use cases, and integration with tools they already use.
   

This is where Google’s A2A launch strategy becomes more telling. To bolster credibility, Google showcased a lineup of early partners supporting the A2A ecosystem. But two major names were noticeably absent:

• Anthropic, the creator of MCP
• OpenAI, who recently adopted MCP as part of their platform strategy
  

Their absence suggests more than scheduling logistics — it hints at competitive divergence, not cooperation.

While both protocols might technically serve distinct purposes, the AI development landscape doesn’t reward parallel standards for long. Builders will gravitate toward the protocol that offers the most leverage — the richest tools, the fastest developer onboarding, the best integrations. In that environment, coexistence becomes a luxury, and consolidation is only a matter of time.

So is Google challenging MCP? Maybe not explicitly. But by launching A2A with its own ecosystem push — and without the backing of MCP’s core advocates — it may have already started the battle.

A2A Vs. MCP Comparison

Why Did Google Build A2A?

In Google’s own words, the motivation behind A2A (Agent-to-Agent) is straightforward:

“To maximize the benefits from agentic AI, it is critical for these agents to be able to collaborate in a dynamic, multi-agent ecosystem across siloed data systems and applications.”

The promise is clear: cross-agent interoperability — even between agents built by different vendors or frameworks — will unlock greater autonomy, productivity, and cost efficiency in AI systems.

By releasing A2A, Google is placing a strategic bet:

The future of AI isn’t just about individual agents — it’s about how those agents work together.

But what makes this move even more notable is the timing.

Just two weeks before A2A’s debut, OpenAI officially adopted MCP — the Model Context Protocol — signaling its commitment to a standard for tool integration and agent context. Then came this public nudge from Google CEO Sundar Pichai:

“To MCP or not to MCP?”
— Sundar Pichai, CEO of Google

One day after A2A’s release, Demis Hassabis, CEO of Google DeepMind, confirmed that Google will in fact support MCP in Gemini models and SDKs — further deepening the story.

Google’s Dual Approach

So now, two protocols are on the table, both backed by AI giants:

• MCP, supported by OpenAI, Anthropic, and soon Google
• A2A, introduced and led by Google
  

At first glance, this might seem like a contradiction — but in reality, it’s a hedge. Google appears to be supporting MCP as a community standard, while simultaneously pushing A2A as its own strategic vision for inter-agent collaboration.

This dual position allows Google to:

• Align with the broader ecosystem (via MCP)
• Shape the architecture of future multi-agent systems (via A2A)
• Avoid platform lock-in while still seeding its own protocol into the space
  

The bigger question is whether developers and the open AI community will rally around A2A, or view it as another proprietary push from a major platform player.

Right now, A2A is more of a vision than a dominant standard — but Google is clearly making moves to turn it into one.

A2A or MCP? Simplicity Will Win

This isn’t the first time we’ve seen a battle between similar technologies with different levels of complexity.

Take the early days of web services, for example. Technologies like XML and SOAP were initially the go-to standards. These formats were highly capable, designed to support large-scale, enterprise-level systems. But they were also complex, cumbersome, and difficult to work with — making them a challenge for developers to adopt and integrate.

Then came JSON (JavaScript Object Notation). While it was simpler, less feature-rich, and not as powerful as SOAP or XML, JSON became the dominant standard for web data exchange. Why? Because it was:

• Easier to work with
  
• More intuitive for developers
  
• Supported widely by the open-source community, which drove its adoption
  

JSON’s simplicity made it the preferred choice, even though it didn’t have all the bells and whistles of its more complex counterparts. It’s a classic example of how simplicity and ease of use often triumph over complexity — especially when it comes to developer adoption.

This lesson holds true in the A2A vs. MCP debate. While both protocols bring unique capabilities to the table, the simpler, more developer-friendly option will likely win out in the long run. If A2A can offer a streamlined, easy-to-implement framework for multi-agent systems, it could quickly overshadow the more complex structures of MCP.

The question, then, is: Which protocol will prove easier for developers to integrate and scale? The answer may determine which technology becomes the standard for multi-agent ecosystems.

A2A and MCP: The Future of AI Agents and Protocols

How will Anthropic respond to A2A? Only time will tell. But given the importance of interoperability in the AI ecosystem, it’s likely that we’ll see a reaction sooner rather than later.

In the meantime, Google seems to be playing its cards carefully by embracing both A2A and MCP. By doing so, it positions itself as a major player in the agent communication space, hedging its bets on a future where both protocols play a role. However, this approach also presents a challenge: the community-driven momentum behind MCP is substantial, and its adoption in the industry is already strong.

While Google is certainly well-resourced and committed to the vision behind A2A, MCP’s widespread adoption within the developer community and among other tech giants puts it in a favorable position — for now.

Ultimately, the real battleground for AI protocols is adoption. The protocol that thrives will be the one that gains traction in real-world use cases, garners tooling support, and attracts developer and community backing. Whether it’s A2A, MCP, or some hybrid approach, the future of AI protocols hinges on which one can prove itself as the easiest, most effective solution for building interoperable, scalable AI systems.

The protocol wars are only just beginning, and we can expect this space to evolve quickly as players like Google, OpenAI, Anthropic, and others continue to build and refine their ecosystems.

Conclusion

A2A and MCP represent two complementary approaches to building more intelligent, scalable, and interoperable AI systems.

A2A is focused on enabling agents to communicate with each other over a network in a way that is secure, asynchronous, and task-centric. This facilitates dynamic coordination between agents, allowing them to work together across various contexts and systems.

On the other hand, MCP is about enriching a model’s ability to reason over external tools and data by injecting structured contextual capabilities directly into the model session. It empowers agents to interact with databases, APIs, and other data systems in a more intelligent and context-aware manner.

Together, these protocols offer a powerful framework for building composable, multi-agent systems that are both extensible and interoperable. By integrating A2A’s communication capabilities with MCP’s context management, developers can create more dynamic, responsive, and intelligent AI workflows that seamlessly connect with a variety of tools, data sources, and other agents.

As both protocols mature and gain adoption, they could play a critical role in the future of AI-powered ecosystems, shaping the way agents, tools, and users interact in increasingly complex environments.

Back to You! 

Want to dive deeper into A2A and MCP? Stay updated on the latest developments in AI protocols and multi-agent systems! Connect with our AI development company – Aalpha Information Systems – for expert insights and solutions.