What Is Agent2Agent (A2A) Protocol And How It Works?

As AI evolves, developers are moving beyond single, monolithic models toward multi-agent systems where specialized AI agents work together. This creates a key challenge: how can agents built by different vendors, frameworks, or platforms communicate seamlessly?

The answer is the Agent2Agent (A2A) protocol.

In this guide, we’ll explain what A2A is, how it works, its core components, how it relates to the Model Context Protocol (MCP), and where it’s being used in the real world.

What is Agent2Agent (A2A) Protocol?

The Agent2Agent (A2A) protocol is an open standard, introduced by Google, that enables secure and scalable communication between independent AI agents. It acts as a universal translator, providing a common language and framework that allows agents to interact and collaborate on complex tasks, regardless of the technology, framework (like LangChain or AutoGen), or vendor that created them.

At its core, A2A treats each AI agent as a networked service with a standard interface, much like how web browsers and servers communicate using HTTP. Released in April 2025 with backing from over 50 technology partners, the protocol is designed to break down the silos that currently exist between agentic systems. 

It allows one agent to discover the capabilities of another, delegate tasks, monitor progress, and receive results in a structured, predictable way.

Agent2Agent (A2A) Protocol vs MCP

A2A and MCP are complementary standards, not competing ones. They solve different but connected challenges in the AI ecosystem. 

Model Context Protocol (MCP) focuses on tool and data access by standardizing how an AI agent connects to external systems such as APIs, databases, calendars, CRMs, and files. 

In contrast, the Agent2Agent (A2A) Protocol focuses on collaboration by standardizing how one AI agent communicates, delegates tasks, and shares results with another AI agent.

In simple terms, MCP handles agent-to-tool communication, while A2A handles agent-to-agent communication. For example, a travel assistant agent might use MCP to retrieve live flight data from an API, then use A2A to send that information to another agent specialized in comparing prices or optimizing itineraries. 

Together, MCP expands what an individual agent can access, while A2A enables multiple agents to work together as a coordinated system.

What are the components of Agent2Agent (A2A) protocol?

The A2A protocol is built around several core components that facilitate structured and transparent communication. Understanding these concepts is key to grasping how agent collaboration works.

A2A Client (Client Agent)

The A2A client, also called the client agent, is the AI system that starts a request or coordinates a workflow. It usually acts on behalf of a user or another application, identifies a task that needs to be completed, and reaches out to other specialized agents for support.

A2A Server (Remote Agent)

The A2A server, or remote agent, is the AI system that receives and processes requests from a client agent. It exposes a standard HTTP endpoint, shares its capabilities, and performs assigned tasks. A core principle of A2A is that the remote agent operates as an opaque system, meaning the client does not need to understand its internal logic, tools, or proprietary infrastructure to work with it.

Agent Card

An Agent Card is a standardized JSON file that acts as an agent’s public profile. It is essential for agent discovery and is commonly hosted at a well-known URL such as /.well-known/agent.json. The Agent Card typically includes the agent’s identity, description, supported capabilities, communication endpoint, authentication requirements, and a list of skills or services it can perform.

Task

A task is the basic unit of work in the A2A protocol. When a client delegates a job, it creates a task with a unique identifier. The task then moves through different lifecycle states such as submitted, working, input-required, and completed, allowing both agents to monitor progress.

Message

A message is a single exchange between the client and server agents within the context of a task. Messages are used to send instructions, request clarification, share progress updates, or deliver final responses during collaboration.

Artifact

An artifact is the final output produced by the remote agent after completing a task. It can be a document, structured JSON data, an image, a report, or any other deliverable generated as a result of the requested work.

Part

A part is the smallest content unit inside a message or artifact. This modular structure allows agents to exchange rich and flexible data formats. Common examples include text parts for written content, file parts for documents or images, and data parts for structured JSON payloads.

How does Agent2Agent (A2A) Protocol work?

An interaction using the A2A protocol follows a structured, logical flow that enables agents to discover, negotiate, execute, and complete tasks reliably. Here, have a look at how A2A works:

Step 1: Discover an agent and read its agent card

The process starts when a client agent needs help with a task outside its own capabilities. It searches for a suitable remote agent by locating and reading its Agent Card. This may happen through a known URL, internal registry, marketplace, or private API directory. The Agent Card tells the client what the agent can do, where to send requests, and what authentication method is required.

Step 2: Inspect agent capabilities

After reading the Agent Card, the client agent understands the remote agent’s supported capabilities, accepted formats, and operational limits. This acts as a lightweight negotiation step, helping the client prepare a request that the remote agent can process successfully without needing manual coordination.

Step 3: Send a task request

The client agent then sends a structured request, typically in JSON format, to the remote agent’s endpoint. This creates a new task with a unique ID and includes the required instructions, inputs, and context. Once received, the remote agent begins processing the assigned work.

Step 4: Track progress

If the task takes time to complete, the remote agent can share progress updates. The client may check status through polling, receive live updates through Server-Sent Events (SSE), or use push notifications sent to a callback URL. These options allow both agents to stay synchronized during long-running workflows.

Step 5: Receive final outputs

When the task is finished, the remote agent marks it as completed and returns the final result as an artifact. This output may include documents, structured data, files, or summaries. If the task cannot be completed, the status is updated to failed or canceled, giving the client a clear final outcome.

What are the benefits of A2A Protocol?

Adopting the A2A protocol offers significant advantages for building scalable and robust AI systems.

• Faster integration: By providing a standard for communication, A2A eliminates the need for custom, one-off integrations between each pair of agents, dramatically reducing development time.
• Reusability and composability: Agents become modular, plug-and-play components. This fosters a marketplace-like ecosystem where developers can combine specialized agents to build new, complex applications.
• Improved robustness: The ability to delegate tasks allows for more sophisticated workflows with clear responsibilities and the potential for fallback options if one agent fails.
• Interoperability and vendor-agnosticism: A2A is an open standard, meaning it prevents vendor lock-in. You can build a system using agents from Google, open-source communities, and other vendors, all working together seamlessly.
• Enhanced security and privacy: With built-in standards for authentication and the principle of opaque agents, collaboration can occur without exposing proprietary logic or sensitive internal data.

Real-world example of A2A Protocol

The true value of the A2A protocol becomes clear in real business workflows where multiple specialized AI agents need to collaborate. Instead of relying on one general-purpose system, organizations can connect expert agents that coordinate tasks efficiently from start to finish.

Recruiting Workflow

In a recruiting workflow, the A2A protocol enables multiple AI agents to work together across the hiring process. A hiring assistant agent may receive a job opening and delegate resume screening to a sourcing agent that matches applicants against required skills and experience. 

Qualified candidates can then be passed to an evaluation agent that ranks profiles, while another scheduling agent coordinates interview slots through calendar systems. 

Once interviews are complete, a feedback agent can collect interviewer notes and summarize next steps. Using A2A, each agent handles a specialized role while staying connected through a shared workflow, making hiring faster, more organized, and easier to scale.

E-commerce Order Management Workflow

In e-commerce, A2A helps coordinate the many systems involved in fulfilling an order. A customer-facing sales agent may receive a purchase request and send it to an inventory agent to confirm product availability. 

If stock is available, a payment agent processes the transaction, while a fraud detection agent reviews risk signals in parallel. After approval, a fulfillment agent triggers warehouse picking and packing, and a logistics agent arranges shipping with tracking updates. 

If delays or returns occur, support agents can step in automatically. With A2A, these specialized agents communicate in real time, reducing errors and improving the overall customer experience.

Travel Booking Workflow

In a travel booking workflow, the A2A protocol allows multiple travel agents to collaborate and build a complete itinerary. A travel assistant agent may begin by understanding the user’s destination, dates, and budget. 

It can then request help from a flight agent to find routes and fares, a hotel agent to compare accommodations, and a transportation agent to arrange airport transfers or car rentals. Another planning agent may recommend attractions, dining, or schedule optimization. 

Once options are confirmed, the agents coordinate bookings and return a unified travel plan. A2A makes the process faster, more personalized, and more efficient than relying on a single general-purpose agent.

Limitations and challenges of A2A Protocol

While A2A is a major step forward, its adoption is not without challenges.

• Standard Drift: As with any open standard, there's a risk that different parties will implement partial or slightly varied versions, leading to compatibility issues.
• Capability Ambiguity: An Agent Card can state that an agent is skilled at "analysis," but evaluating the actual quality and reliability of that agent's work remains a challenge.
• Performance Overhead: Chaining multiple agent-to-agent calls can introduce latency, making it difficult to guarantee performance in real-time applications.
• Security Risks: While A2A has security in mind, delegating tasks and sharing context across multiple agents inherently expands the potential attack surface for issues like data leakage or prompt injection.

Read more: The Infrastructure for an Agent-to-Agent Economy

Conclusion

The Agent2Agent (A2A) protocol is emerging as a foundational standard for the future of AI. By providing an open and standardized way for AI agents to communicate, coordinate, and delegate tasks, A2A enables intelligent systems that are far more capable than isolated single-model solutions.

As multi-agent architectures continue to grow, A2A will play a critical role in building secure, interoperable, and scalable AI ecosystems. When combined with complementary standards like MCP, it creates the framework organizations need to develop enterprise-ready AI systems that can collaborate efficiently across tools, platforms, and workflows.

About TrueFoundry: 

TrueFoundry is an enterprise AI platform that helps teams build, deploy, and scale production-ready AI applications securely. Its AI Gateway provides unified access to multiple LLMs with controls for routing, security, and cost management, while the MCP Gateway enables standardized, secure connections between AI agents, tools, and enterprise systems.

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