How Google A2A Protocol Actually Works: From Basic Concepts to Production

Google's A2A protocol has launched with strong support from more than 50 tech partners. Major players like Atlassian, PayPal, and Salesforce have already joined the initiative. Their involvement marks a radical alteration in the way AI agents work together and communicate on enterprise platforms.

The fragmented AI ecosystem needs standardized communication as specialized AI services become more prevalent. A2A protocol tackles this challenge head-on by offering a well-laid-out framework. AI agents can now securely share information and coordinate their actions. The protocol uses concepts like Agent Cards, Tasks, and Artifacts to define how agents can find capabilities and handle interactions on platforms of all types.

This piece will get into the workings of the A2A protocol - from basic concepts to production implementation. You'll learn about its communication methods, security features, and ground applications that show its practical value for enterprise AI integration.

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Understanding the Purpose of Google A2A Protocol

"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." — Google (official documentation), Google Cloud, A2A protocol announcement

AI is changing faster than ever. The field has moved beyond single-agent solutions to complex multi-agent ecosystems. A key question arises: How can agents from different vendors work together?

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Why agent interoperability matters in multi-agent systems

AI agents working together brings many complex challenges. These agents need strong frameworks that go beyond technical compatibility. They must address security, governance, and adaptability. Without common standards, agents work in isolation. This creates broken workflows and missed chances to work better together.

Companies now use more specialized AI systems. These systems need to work together more urgently than before. Google's team pointed out that "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".

Agent interoperability offers several benefits:

• Increased efficiency: Agents that blend across platforms boost productivity and cut long-term costs
• Framework flexibility: Companies can pick the best specialized agents whatever their underlying frameworks
• Simplified integration: Common protocols make it easier to connect different AI systems
• Enterprise-wide automation: Makes shared processes possible across departments and systems

Real-life business tasks rarely happen in isolation. Take recruitment as an example. Different AI agents handle finding candidates, scheduling interviews, and checking backgrounds. Without common communication rules, getting these specialized agents to work together becomes too complex.

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What is A2A and how it is different from MCP

Google's Agent2Agent (A2A) Protocol launched in April 2025 as an open standard to solve these connection challenges. More than 50 technology and consulting partners helped develop it—including Atlassian, Salesforce, Accenture, Deloitte, and PayPal. A2A creates a shared language for AI agents whatever their underlying frameworks.

A2A helps what Google calls a "client agent" and a "remote agent" communicate. The client agent develops and shares tasks. The remote agent acts on these tasks to provide information or take needed actions. This setup lets agents work together without sharing their internal logic or memory. They stay independent while cooperating effectively.

Both protocols deal with agent capabilities, but A2A works differently from Anthropic's Model Context Protocol (MCP). Google sees these protocols as complementary tools rather than competitors. Google officially states that "Agentic applications need both A2A and MCP".

The main difference lies in their goals:

• MCP offers standard, secure context for individual agents. It focuses on connecting models to tools and data sources
• A2A lets independent agents communicate. It focuses on how agents find and interact with each other

MCP connects older data systems and APIs with LLM-based applications. A2A manages how agents talk to each other. Together, they build a complete ecosystem. Agents can access resources through MCP while working with other agents through A2A.

A2A builds on web standards that companies already know—HTTP, Server-Sent Events (SSE), and JSON-RPC. This makes it compatible with existing IT systems. Google designed it this way to make technology "easier to integrate with existing IT stacks businesses already use daily".

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Core Components of the A2A Protocol

Google A2A protocol's technical architecture builds on several core components that make shared communication between agents work. The protocol differs from regular APIs by creating a standard framework with clear structures for agent interactions.

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AgentCard structure and discovery endpoint

The A2A's discovery system uses an AgentCard at its heart. This standard JSON metadata file sits at /.well-known/agent.json from an agent's base URL. The digital identity card works like web browsers finding robots.txt files and creates a consistent spot to store capability information across the network.

Client agents need specific fields in the AgentCard to make meaningful connections:

• Name and description of the agent
• Endpoint URL to receive A2A requests
• Authentication requirements and security schemes
• Protocol version compatibility information
• Capabilities flags (streaming, push notifications, state history)
• Skills array detailing specific functionalities offered

This self-documenting system removes the need for manual setup. Other agents can read this machine-readable "résumé" to decide if and how they should connect.

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Task lifecycle: submitted → working → completed

Tasks are the central concept in A2A. These stateful entities track work between agents. Each task moves through specific states:

• submitted: Task received but not yet started
• working: Active processing underway
• input-required: Agent needs additional information
• completed: Task successfully finished
• canceled: Task terminated before completion
• failed: Task encountered unrecoverable error
• unknown: Indeterminate state

Every state change comes with a timestamp and might have context messages. Agents can stay in sync on task progress through this organized approach, especially when operations take hours or days.

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Message and Part types: TextPart, FilePart, DataPart

Messages power communication in A2A. They represent single conversation turns with one or more Parts. Each message has a role field that shows if it came from the "user" (client) or "agent" (server).

The protocol has three basic part types:

• TextPart: Holds plain text content in the text field
• FilePart: Shows binary data through a file object with base64-encoded bytes or a uri pointing to the file location
• DataPart: Contains structured JSON information in the data field, perfect for forms and structured results

This multi-part message design lets agents share rich, flexible interactions beyond text and supports complex data exchange.

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Artifacts and their role in task output

Artifacts are the final outputs that agents create during task execution. These structured content deliverables differ from regular messages and contain:

• Optional name and description fields
• Array of content parts with specified formats
• Index value for proper sequencing
• Append flag showing if content should add to existing artifact
• LastChunk marker for final segments in streaming scenarios

Artifacts can also have metadata that provides extra context about the generated content. This system supports everything from simple text responses to complex outputs with images, audio, or structured data.

The Google A2A protocol creates a consistent framework for agent interaction by standardizing these components. The underlying implementations or frameworks don't matter.

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Communication Flow in A2A Tasks

The Google A2A protocol gives you several ways to handle tasks of different complexity and duration. Simple request-response cycles and persistent streaming connections provide flexibility for agents to interact with each other.

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tasks/send vs tasks/sendSubscribe explained

The A2A protocol uses two main methods for task communication that serve different purposes:

tasks/send uses a synchronous request/response pattern that works best for quick tasks that finish right away. The client agent waits for the remote agent to process the task completely before getting a response. This works great for simple queries but isn't the best choice for longer tasks where clients need to keep checking the status.

tasks/sendSubscribe creates a streaming connection that gives you live updates as the task moves forward. This method sets up a lasting connection so the remote agent can send updates without needing multiple requests from the client. This approach works better for:

• Tasks that produce step-by-step output
• Operations that need progress tracking
• Interactive sessions that need quick feedback

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Streaming updates using Server-Sent Events (SSE)

A2A uses Server-Sent Events (SSE) for streaming connections. SSE is a standard web technology that lets servers send data to clients in one direction. It runs over HTTP with the Content-Type: text/event-stream header and all major browsers have supported it since January 2020.

The server keeps the HTTP connection open when a client starts a task through tasks/sendSubscribe. Updates get pushed through SSE as events happen. These updates usually include:

• TaskStatusUpdateEvent: Shows state changes (working → completed)
• TaskArtifactUpdateEvent: Delivers partial or final results

SSE's simple design makes it perfect for live task monitoring without WebSocket connection overhead. This lets clients get immediate feedback throughout the task's execution instead of waiting until it's done.

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Push notifications via webhook configuration

A2A supports push notifications for cases where keeping a connection open doesn't make sense—like very long-running tasks. Client agents can register a webhook URL to receive their updates.

The process is straightforward:

1. Client calls tasks/pushNotification/set with webhook configuration
2. Client starts the task normally through tasks/send
3. Remote agent sends HTTP POST requests to the webhook URL as updates happen

Your webhook configuration needs:

• URL endpoint to receive notifications
• Security token to authenticate
• Optional authentication scheme details

This "fire and forget" approach helps clients handle tasks that take hours or days without keeping connections open. Push notifications work alongside streaming to create truly asynchronous workflows in business environments.

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Materials and Methods: JSON-RPC and HTTP Transport

Google A2A protocol uses time-tested web technologies as its backbone. This design choice makes it easier to integrate with existing enterprise systems and creates standardized communication paths between different agent implementations.

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Protocol transport layer: HTTP + JSON-RPC

The A2A protocol stands on two proven technologies: HTTP as the transport layer and JSON-RPC 2.0 as the message format. Most IT teams already know and use these technologies. Production systems must use HTTPS with modern TLS ciphers to keep communications secure.

JSON-RPC lets systems make remote procedure calls using JSON, regardless of programming language. Every A2A message follows this structure:

This combination with HTTP creates a standard pathway for agents to call methods, pass parameters, and get results without worrying about implementation specifics.

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Authentication schemes and security model

A2A comes with enterprise-grade authentication schemes that match OpenAPI specifications. Each agent's AgentCard lists its supported authentication methods (OAuth, API Keys, etc.). Clients can then pick the authentication mechanism that works best.

The authentication happens separately from the A2A protocol flow. Clients first talk to authentication authorities, then add their credentials to HTTP requests based on the chosen schemes. Servers check each request and respond with standard HTTP status codes (401, 403) when needed.

A2A also uses detailed authorization through role-based access control. This two-layer security approach ensures only properly verified and authorized agents can join workflows or access protected data streams.

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Agent server and client implementation overview

A2A implementation has two main parts:

A2A Client - manages request sending and response handling according to JSON-RPC specifications. It handles standard and streaming responses, including Server-Sent Events for up-to-the-minute updates. Clients can use custom fetch implementations for different environments.

A2A Server - handles incoming requests through an AgentManager object that runs tasks. You can start it with new A2AServer(myAgentLogic, { taskStore: store }) and activate it using server.start(). The server listens on port 41241 by default.

Both components need proper error handling for network issues and JSON-RPC errors. They also need detailed logging to monitor and debug effectively.

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Results and Discussion: Real-World Use Cases and Limitations

"We are already leading the way in the A2A space by focusing on industry solutions that provide real business value—saving time, reducing overhead and helping our clients drive revenue and enhance processes like the development of FDA documentation during the drug discovery process." — Marc Cerro, VP of Global Google Cloud Partnership at EPAM

Real-life applications of Google A2A protocol show its strengths and where it needs improvement. Looking at how A2A works in practice helps us understand its functions beyond theory.

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Candidate sourcing workflow using A2A

Research showed that 73% of automation projects failed because AI tools couldn't work together properly. Google responded by creating a practical recruitment workflow that showed A2A's true potential. The process starts when a hiring manager asks their assistant agent to search for specific candidates. Here's how it works:

1. The HR assistant agent connects with a recruiting agent (often linked to LinkedIn)
2. These agents work together through A2A to understand job needs
3. A scheduling agent steps in to set up interviews once candidates are found
4. A background check agent does the final verification of credentials

This process used to take 72 hours of manual work. Now A2A-enabled agents complete it in minutes. Each agent keeps its specialized intelligence and doesn't just act as a simple tool.

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Limitations in dynamic UX negotiation

A2A brings a fresh approach to user experience negotiation, but it has its limits. Agents can discuss how to present content using message "parts" with specific MIME types. The system struggles with:

• No standard way to handle interactive elements on different platforms
• Problems managing complex UI needs
• Poor tools to adapt UI in real time

The protocol is new and best practices continue to develop.

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Challenges in agent capability discovery

The Agent Card system is powerful but faces several key issues:

• No way to guarantee if capabilities are accurate or available
• Security risks during discovery
• Hard to keep capability updates in sync across systems

The protocol must balance showing full capabilities while staying secure. Developers new to the protocol face extra complexity because the Agent Card system needs regular credential updates to stay safe.

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Conclusion

Google's A2A protocol solves many enterprise AI integration challenges. The protocol uses standardized components like Agent Cards, Tasks, and Artifacts that create a common language for AI agents on different platforms and vendors.

A2A manages various communication patterns effectively. These range from basic request-response cycles to advanced streaming updates. The protocol builds on 10-year old technologies like HTTP and JSON-RPC. This ensures system compatibility while you retain control through complete authentication schemes.

Ground applications, especially the candidate sourcing workflow, show A2A's practical value. The protocol has limitations in areas like dynamic UX negotiation and capability discovery. However, its architecture allows for improvements and future growth.

Major technology partners have widely adopted A2A, showing strong industry trust in its approach. AI services continue to specialize and grow. A2A's standardized framework will become vital to enable uninterrupted agent collaboration on enterprise platforms.

A2A will affect more than just technical integration in the future. It brings a radical alteration in how AI systems collaborate. This protocol enables sophisticated multi-agent workflows that will increase efficiency and automation for businesses using AI technology.

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FAQs

Q1. What is the Google A2A Protocol?

The Google A2A (Agent-to-Agent) Protocol is an open standard designed to enable communication and collaboration between AI agents from different vendors. It provides a structured framework for agents to exchange information and coordinate actions securely across various platforms.

Q2. How does A2A differ from other AI communication protocols?

A2A focuses specifically on enabling communication between independent AI agents, while other protocols like MCP (Model Context Protocol) concentrate on how individual models connect to tools and data sources. A2A is designed to work complementarily with these other protocols to create a more complete AI ecosystem.

Q3. What are the key components of the A2A Protocol?

The core components of A2A include AgentCards for capability discovery, a defined task lifecycle, various message and part types for communication, and artifacts for task outputs. These elements work together to create a standardized way for agents to interact and share information.

Q4. How does A2A handle real-time communication between agents?

A2A supports real-time communication through streaming updates using Server-Sent Events (SSE). This allows for continuous, one-way server-to-client communication, enabling agents to receive immediate feedback and updates throughout task execution.

Q5. What are some practical applications of the A2A Protocol?

A2A has been successfully applied in various enterprise scenarios, such as streamlining recruitment processes. For example, it enables multiple specialized AI agents to collaborate on tasks like candidate sourcing, interview scheduling, and background checks, significantly reducing the time and effort required for these workflows.