Introduction
AI systems are evolving from single large models into ecosystems of tools and agents that can reason, delegate tasks, and collaborate. With this shift comes a need for standard ways for these components to communicate.
In 2024, Anthropic introduced the Model Context Protocol (MCP) as an open standard to connect AI assistants with external tools, data sources and options to leverage client LLMs (sampling). Shortly after, in April 2025, Google announced Agent-to-Agent (A2A) – an open protocol designed to let autonomous AI agents talk to each other directly.
A2A is allegedly complementary to Anthropic’s MCP: while MCP gives AI agents “hands and eyes” by plugging into tools and data, A2A gives agents a common language to collaborate with one another across different platforms.
This post will break down what the A2A protocol is, how it works, why it was developed, and how it compares to MCP in architecture and usage. We’ll also explore when you’d use A2A instead of (or alongside) MCP, with practical examples.
What is the Google A2A Protocol?
Google’s Agent-to-Agent (A2A) Protocol is a new open standard for enabling AI agents to communicate and work together. In essence, A2A defines a clear method for two or more intelligent agents to interact over regular web protocols (HTTP) – one agent can ask another to perform a task, get a result back, and they maintain a structured conversation while doing so.
The idea is to unlock dynamic multi-agent ecosystems in enterprises and beyond, where different AI agents need to cooperate on complex tasks across siloed systems, which goes beyond the MCP concept of having a more or less centralized client LLM.
How A2A Works
A2A uses a simple client–server model between agents. Any agent can play the role of a client or remote agent depending on the context. The protocol builds on familiar standards – it runs over HTTP with structured JSON messages (using a JSON-RPC schema) for requests and responses.
Each A2A-compatible agent exposes a sort of “digital business card,” called an Agent Card (a JSON file). This card advertises the agent’s capabilities (skills), address (URI), version, and any auth requirements. An agent that wants to use another will first fetch this card to discover what the other agent can do and how to call it.
Once discovered, agents communicate by exchanging tasks and messages. A “task” in A2A encapsulates a request from one agent to another – for example, Agent A asks Agent B to fetch weather info or analyze a dataset. The task has a lifecycle managed by the protocol: it can be pending, in progress, require more input, or completed with a result (the result is called an artifact in A2A terminology).
The agents send messages back and forth associated with the task, which carry content (text, data, or other media) divided into parts. This messaging allows ongoing collaboration: agents can clarify the request, stream intermediate results, or ask follow-up questions if needed.
Sync and Async Patterns
Critically, A2A supports flexible communication patterns to suit different task durations and interaction styles.
- Simple synchronous calls: For quick requests, an agent can make a straightforward HTTP call and get an immediate response.
- Polling / Long-Polling: For longer-running tasks, the client agent can check back for status updates.
- Server-Sent Events (SSE): The remote agent can stream progress updates or partial results back to the client in real time for short interactive tasks.
- Push Notifications / Webhooks: For very long tasks (minutes or hours), the remote agent can notify the client when the job is done.
Security and Trust
A2A was designed with enterprise security in mind. Agents can be deployed across organizations or cloud environments, so authentication and authorization are built-in. The protocol supports standard auth schemes (OAuth tokens, etc.) to ensure agents only accept requests from authorized parties. In practice, an Agent Card can specify what auth method to use (e.g., a token or key) before the remote agent will process tasks for it. This way, companies can trust that their agents collaborate securely even in a broader network.
Why A2A?
A2A’s goal is to enable “true multi-agent scenarios without limiting an agent to a tool”. By standardizing agent-to-agent dialogue, A2A makes it easier to plug in an agent from one vendor with another, or let a planning agent delegate subtasks to various specialist agents. Google’s announcement highlights use cases like enterprise workflows where, for example, an HR agent, a finance agent, and an IT agent might need to coordinate on onboarding a new hire. In such scenarios, A2A provides the common language for that coordination. Because some multi-agent tasks can be complex and long-running, A2A explicitly accommodates those with its async update features.
In summary, A2A provides a framework for agent collaboration: Agents register what they can do in a standard way, talk to each other over HTTP using structured messages, handle long or short tasks gracefully, and do it all securely. It turns independent AI agents into services that can orchestrate together to solve problems.
A quick recap on MCP
Model Context Protocol (MCP), introduced by tackles a different problem: how to connect an AI assistant to the wide world of external tools, data, and real-time information. If A2A is about agents talking to agents, MCP is about an agent reaching out to tools and data sources. It’s often described as a sort of “universal adapter” – a standard way to plug many different data sources or services into an AI.
MCP follows a classic client–server architecture. The client is typically an AI assistant or an LLM-based agent that needs something it can’t get from its built-in knowledge – such as an up-to-date database record, a user’s email inbox, or the ability to execute code. The server in MCP is a connector or wrapper around a specific tool or data source. For instance, you might have an MCP server for a SQL database, one for a Google Drive, another for a GitHub repository, etc. Each MCP server exposes certain capabilities of that external system in a standardized way.
A2A vs. MCP – Key Differences
| Dimension | Google A2A Protocol | Anthropic MCP |
|---|---|---|
| Architecture Model | Decentralized multi-agent network. Each AI agent runs as an independent service, exposing a public interface so others can discover its skills and endpoint. Communication is peer-to-peer in concept (one agent calls another over HTTP). Any agent can be a client (initiator) or server (executor) for a task. | Client–server. The AI assistant (LLM) acts as a central client and connects to one or many MCP server endpoints. Each server is a connector or plugin that exposes a specific tool or data source. The architecture is modular but centralized around one primary LLM which uses various tools and resources as needed. |
| Primary Purpose | Horizontal integration – agent collaboration. A2A was built to let multiple intelligent agents coordinate tasks among themselves. It shines in scenarios where no single agent has all the knowledge or abilities, and they need to work together (e.g. one agent plans a solution and delegates subtasks to others). The focus is on communication between autonomous agents (including cross-vendor or cross-system agents). | Vertical integration – tool/service integration. MCP’s goal is to give a single AI agent access to external information and actions it couldn’t otherwise perform[33][34]. It addresses the problem of feeding relevant context into an AI and executing tasks like API calls or database queries on the AI’s behalf. The focus is on connecting an agent to various external tools and data sources, rather than connecting agents to each other. |
| Communication Style | Agents communicate through high-level requests and responses wrapped as tasks and messages. The content can be natural language or structured data, but the protocol defines a standard message format (JSON-RPC) for these exchanges. Interactions can be multi-turn: an agent might send a task, receive a partial answer or follow-up question, send additional info, and so on until the task is resolved. | Communication in MCP is more like remote procedure calls or API calls. The AI (client) invokes a function on a tool with some parameters and gets back a result. These calls are often triggered by the AI model itself when it decides it needs a tool, and the surrounding system (like Claude’s runtime) handles the JSON-RPC exchange with the MCP server. The pattern is usually request → response, which then gets incorporated into the AI’s context. |
| Trust & Security | Designed for cross-boundary trust. Because A2A connects agents which might live in different organizations or cloud environments, it emphasizes standard auth and permission controls. Agent Cards advertise what auth is required (e.g. API keys, OAuth tokens). Essentially, an agent can’t just call any other agent arbitrarily – it must be authorized per the remote agent’s policy. A2A’s threat model thus considers external actors – similar to securing an API endpoint on the internet. | Designed for controlled environments. MCP typically runs within a user’s own environment or between services that a single organization controls. The trust assumption is that the user/admin chooses which MCP connectors to enable and provides the necessary credentials (e.g., API keys for those tools). So the AI will only access data that the user has explicitly allowed. The communications are secured (often happening on localhost or through encrypted channels). The main trust concern is ensuring the AI doesn’t misuse its tool access, which is managed by limiting what each MCP server can do. |
| Latency Expectations | Adaptive to task length. A2A is built to handle everything from quick queries to long-running jobs that might span minutes or even involve human-in-the-loop delays. | Optimized for real-time assistance. MCP calls are usually expected to be fast, since they occur during an AI’s response generation. |
| Typical Use Cases | Agent orchestration & collaboration: Wherever multiple specialized AIs need to cooperate. For example, in an enterprise, a “Project Manager” agent might coordinate a “Coding” agent, a “Testing” agent, and a “Documentation” agent to complete a software project. Or as Google’s example shows, a hiring workflow might involve one agent sourcing candidates, another scheduling interviews, and another conducting background checks – all negotiating via A2A. | Tool use & context injection: Whenever an AI assistant needs to pull in outside information or take actions on behalf of the user. Common examples include an AI data analyst querying a database and visualizing results, an AI customer support agent looking up your order status in a CRM, or an AI writing assistant fetching relevant documents from your Google Drive for reference. MCP is ideal for enhancing chatbots and assistants with live knowledge and capabilities. |
Do you want to upgrade your data products to enable agent-based communication? Official Google Release: https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability Get in touch: https://evo-byte.com/contact/