Agent2Agent Architecture: A New Era of Agent Collaboration.

AI agents are changing the way we get things done. These digital assistants can take over many time-consuming or complex tasks, like answering emails, helping with customer support, managing orders, or planning logistics, without needing constant human supervision. This allows people to focus on more important or creative work, improving overall productivity.

More and more companies are now using agentic AI to boost efficiency, automate routine processes, and streamline daily operations. Whether it’s helping an employee order a new laptop, guiding a customer service rep through a tricky issue, or supporting decision-making in supply chain management, agents are becoming valuable members of the digital workforce.

However, to get the full value from these intelligent systems, it’s not enough for them to work alone. The real magic happens when agents can work together, even if they come from different vendors, use different programming tools, or are connected to separate systems. If AI agents can seamlessly share information and collaborate across different environments, businesses can unlock even more automation, reduce manual work, and save costs in the long run.

In this blog, we’ll explore how Google’s Agent2Agent (A2A) protocol helps solve this challenge by enabling agents to communicate and collaborate effectively, no matter how or where they were built. We’ll walk through how it works, its key features, and why it’s a game-changer for building smarter, more connected AI systems across organizations.

What is Agent2Agent Protocol?

Google’s Agent2Agent protocol, released on April 9, 2025, is a new communication standard that helps different AI agents work together smoothly. Even if they were created by different developers or built different tools and platforms.

Think of A2A like a universal language for AI agents. Just like how humans from different countries might use English to communicate, A2A gives AI agents a shared way to understand each other’s messages, requests, and responses. This solves a major problem in the AI world today: many AI systems are isolated or only work within their own ecosystems, making it hard for them to collaborate or exchange information.

With A2A, agents can:

• Talk to each other using a standard format.
• Share tasks and help each other complete them.
• Work across systems, even if they were made using different programming languages or frameworks.

Agent2Agent protocol makes it possible to connect AI agents like puzzle pieces, even if those pieces come from different companies. It’s open-source, meaning anyone can use it or contribute to its development. This makes the AI ecosystem more flexible, powerful, and connected, encouraging innovation and teamwork between agents across the tech world.

Agent2Agent Design Principles

Agent2Agent is an open, interoperable protocol specifically built to enable seamless collaboration between agents, regardless of the platforms, frameworks, or vendors they originate from. While developing this protocol in close collaboration with our partners, we followed five core design principles that shaped every decision. These principles ensure that A2A remains flexible, scalable, and ready for real-world enterprise use.

Empowering Agentic Capabilities

At its core, Agent2Agent is designed to fully embrace the autonomy and natural communication styles of agents. Unlike traditional integrations that require shared tools, memory, or a common environment, A2A allows agents to interact organically even when they have no prior shared context. This principle ensures agents can operate in a loosely coupled way, communicating effectively without rigid dependencies.

It is enabling true multi-agent ecosystems, where agents can dynamically collaborate, delegate tasks, and share information without being limited to acting as a simple “tool” or extension of another system. This opens the door to sophisticated agent networks where each agent can contribute independently based on its strengths.

Offering Established Standards

To accelerate adoption and reduce integration overhead, Agent2Agent is built for or widely adopted web and data exchange protocols such as HTTP, Server-Sent Events (SSE), and JSON-RPC. These are standards that most enterprise systems already support, which means businesses don’t need to overhaul their existing infrastructure to adopt A2A.

By building on proven technologies, ensure developers can quickly plug A2A into existing IT environments, reducing friction and speeding up time-to-value. This choice also future-proofs the protocol, allowing it to evolve alongside well-supported and maintained industry standards.

Secure by Default

Security is non-negotiable, especially in enterprise environments where sensitive data and workflows are involved. That’s why A2A is designed with enterprise-grade security built in from day one. It supports robust authentication and authorization mechanisms, aligning with the widely respected OpenAPI security schemes.

This principle ensures that all agent interactions are protected by standardized access controls, allowing only verified and authorized entities to participate in the communication flow. By default, every exchange is secure, reducing the risk of unauthorized access, data leaks, or impersonation attacks.

Support for Long-Running Tasks

A2A isn’t just for quick exchanges. It’s built to handle both short, instant interactions and complex, long-duration processes. Whether agents are coordinating on a few-second task or engaging in multi-phase research or decision-making that spans hours or even days, A2A is equipped to manage it efficiently.

The protocol allows for real-time feedback, status updates, and progress notifications throughout the entire lifecycle of a task. This ensures that users or supervising agents stay informed and can intervene when needed. It also supports scenarios where human input is part of the loop, enabling hybrid workflows that combine the best of human judgment and agent efficiency.

Modality Agnostic by Design

Agents today operate in a rich, multimodal world, not just text. Recognizing this, A2A was built from the ground up to be modality agnostic. This means it can support various forms of input and output, including audio, video, images, and real-time streaming data.

By enabling agents to interact across multiple communication channels, A2A expands the types of experiences developers can create. Whether it’s a voice assistant collaborating with a visual analysis agent or a video summarization tool coordinating with a chatbot, A2A can seamlessly connect these components without forcing them into a single mode of communication.

Core Architecture of the Agent2Agent Protocol

The Agent2Agent (A2A) protocol provides a powerful and extensible technical foundation for enabling communication between AI agents, no matter which platform, vendor, or underlying framework they’re built on. At its heart, A2A is designed to foster interoperability and agility across multi-agent systems by establishing standardized communication flows, structured message formats, and reliable transport mechanisms.

This architecture empowers agents to interact securely, efficiently, and autonomously, paving the way for scalable, collaborative AI ecosystems that can adapt to real-world enterprise demands.

Client-Remote Agent Communication Model

The core architectural model of A2A is centered around two distinct agent roles: client agents and remote agents. This division of responsibilities allows each agent to focus on its specialized function, making the overall system more modular, adaptable, and performance-optimized.

• Client agents are the initiators. They identify the need for external expertise, formulate a request, and send it to the most appropriate remote agent.
• Remote agents, on the other hand, are the executors. They receive task requests, process them using their unique capabilities, and then return responses, artifacts, or action outcomes to the client.

This separation ensures that tasks are handled by the most capable agents without requiring a shared memory or centralized toolset. Agents communicate through structured messages rather than relying on internal dependencies. This approach, often referred to as “agentic-first design,” ensures that agents retain their autonomy while still being able to collaborate effectively across teams, domains, and even organizations.

Here’s a high-level view of how the communication process flows:

• Task Identification: A client agent detects a task that requires outside help, either due to limitations in its scope or the complexity of the task.
• Capability Discovery: It searches for a remote agent with the required skills or services, leveraging A2A’s discovery mechanisms.
• Task Dispatch: Once an appropriate remote agent is identified, the client sends a structured task request.
• Task Execution: The remote agent performs the task, which may involve analysis, content generation, or even real-world action.
• Response Handling: The result, known as an artifact, is returned to the client agent, which uses the response to move forward in the workflow.

This model supports everything from quick, one-off queries to long-running, asynchronous collaborations that may span hours or days, depending on the complexity of the task.

JSON-RPC 2.0 for Message Exchange

To manage communication between agents, A2A leverages JSON-RPC 2.0, a lightweight, widely supported remote procedure call protocol based on the JSON data format. This strategic choice ensures that the message exchange process remains language-agnostic, simple to implement, and highly extensible.

Each interaction between agents is composed of structured messages, which are made up of “parts,” self-contained chunks of content that can be text, images, audio, or other data types. These parts make agent-to-agent communication more flexible and expressive, especially in multi-modal environments.

Here’s how JSON-RPC enhances communication:

• It defines standard methods and parameters, allowing agents to understand each other’s intent clearly.
• It maintains a uniform structure for requests and responses, so agents don’t need to know how the other side is implemented internally.
• It supports both synchronous and asynchronous interactions, depending on the task type and complexity.

By standardizing how tasks are sent and responses are received, Agent2Agent significantly reduces integration complexity. Developers can focus on building agent logic rather than worrying about the technicalities of communication protocols.

HTTP and SSE for Transport and Real-Time Communication

At the transport layer, Agent2Agent uses HTTP, the same protocol that powers the modern web. This makes it incredibly easy to integrate A2A into existing enterprise infrastructure and development environments. By using familiar web protocols, A2A lowers the barrier to entry and increases accessibility for developers and IT teams alike.

To support long-running tasks and enable real-time feedback, Agent2Agent incorporates Server-Sent Events (SSE). This allows remote agents to push updates to client agents as tasks progress, eliminating the need for constant polling or manual refreshes.

Key advantages of this setup include

• Real-time updates: Agents can subscribe to task progress using `tasks/sendSubscribe`, enabling live monitoring.
• Webhook-based notifications: Client agents can register secure webhook endpoints to receive push notifications when tasks are completed or updated.
• Efficient media streaming: SSE allows smooth delivery of large data payloads such as audio, video, or generated documents in real time.

This transport design ensures that even during complex, multi-phase tasks like recruitment automation and strategic business planning, agents can maintain continuous collaboration without interruptions.

Protocol Versioning and Backward Compatibility

To support long-term scalability, A2A includes a robust protocol versioning system. Each version defines the features, capabilities, and compatibility rules that agents must follow. This makes it possible for older agents to continue functioning even as the protocol evolves.

For example:

• A client agent operating on version 1.0 can still collaborate with a remote agent on version 1.2, as long as they both adhere to the same compatibility level.
• Version tags allow developers to gradually adopt new features without having to overhaul or rewrite existing agents.

This versioning approach protects technical investments while promoting innovation. It ensures that the Agent2Agent ecosystem remains future-proof, with space for continuous improvement and evolving agent behaviors.

Capabilities Discovery Mechanism in Agent2Agent Protocol

The Capability Discovery Mechanism is a foundational element of the Agent2Agent protocol. It’s what allows intelligent agents to not only recognize each other across organizational or vendor boundaries but also to understand what each agent is capable of doing. This mechanism makes cross-ecosystem agent collaboration not just possible, but purposeful and efficient.

By allowing agents to broadcast their capabilities and search for others’ competencies, A2A builds a shared language for cooperation, enabling distributed agents to work together dynamically and with intent.

Agent Card: The Digital Identity of an Agent

At the center of this discovery process is the Agent Card, a standardized metadata document that serves as the agent’s digital résumé within the A2A ecosystem. Just like websites use `robots.txt` or APIs expose `swagger.json`, A2A agents publish their capabilities through a JSON file available at a predictable path: `/.well-known/agent.json`

This agent card contains all the important information a client agent needs to evaluate whether collaboration is possible or desirable.

Key components of the agent card include:

• Functional capabilities: A clearly defined list of actions or services the agent can perform.
• Endpoint URL: A designated web address where Agent2Agent task requests should be sent.
• Authentication protocols: Security parameters that specify what level of authentication is required to interact with the agent.
• Protocol version compatibility: Metadata indicating which versions of the Agent2Agent protocol the agent supports.

Because the agent card uses strictly formatted JSON, it remains both machine-learning readable and human-understandable. This consistency enables agents from different vendors and platforms to interoperate seamlessly, regardless of internal implementations. And while the core structure is standardized, the format allows for extension, meaning AI developers can add custom fields to support specialized use cases without sacrificing compatibility.

Dynamic Capability Registration: Evolving in Real Time

Unlike traditional services of AI agent registries that rely on static configurations, the Agent2Agent protocol introduces a dynamic capability registration model. This approach gives agents the flexibility to advertise new services or modify existing ones on the fly, without interrupting ongoing communication or requiring system restarts.

Here’s how it works:

• Agents publish their capabilities by updating the agent card.
• New skills or functions can be added in real time, instantly becoming visible to other agents.
• Existing capabilities can be modified or deprecated without disrupting tasks already in progress.

This dynamic nature is a major leap forward in building self-adaptive agent networks. It allows ecosystems to scale fluidly and incorporate new capabilities as they become available, without breaking established workflows.

Moreover, remote agents retain full control over how their capabilities are exposed. They can implement contextual access control, meaning that the list of visible capabilities can vary depending on the identity or authorization level of the client agent. For instance, a remote agent might show a basic capability list to anonymous users but a more advanced set of functions to verified clients. This level of control ensures that sensitive capabilities are protected and shared responsibly.

Advanced Querying with Capability Filters

With potentially thousands of agents in an ecosystem, client agents need tools to efficiently discover the right capability for the task at hand. To meet this need, Agent2Agent protocol supports a sophisticated filtering system based on query parameters, making capability discovery precise, scalable, and developer-friendly.

Agent2Agent’s capability queries support:

• Equality filters: Find capabilities that match a specific value.
• Range queries: Search for numeric values within defined limits (e.g., price, latency)
• Regex pattern matching: Perform advanced string searches using regular expressions.
• Multi-criteria logic: Combine multiple filters using logical operators like AND/OR.

This filtering mechanism borrows concepts from traditional database query languages, making it easy for developers to adopt without a steep learning curve. For example, client agents can search for services based on data types supported (text, image, audio), authentication level, or even latency thresholds.

To ensure performance and prevent misuse, the Agent2Agent protocol imposes some reasonable limits:

• URL length is capped at 2000 characters.
• Filter complexity is limited to around 10 combined conditions.

These boundaries keep queries lightweight and efficient without restricting legitimate discovery operations.

Additionally, advanced clients can use comparison operators (`>`, `<`, `=`, `!=`) in combination with the `property` query field to fine-tune their searches. This allows for deep capability exploration in large agent ecosystems, making it easier to find exactly the right agent for a complex job.

In comparison to older or more rigid agent communication systems, Agent2Agent’s approach represents a significant advancement. It enables smart, context-aware agent collaboration that aligns with the fast-paced, decentralized nature of modern artificial intelligence ecosystems.

Key Features and Benefits of Agent2Agent Protocol

Standardized Communication Layer

Agent2Agent establishes a consistent and structured communication model for agents, leveraging familiar web protocols. It uses HTTP as the primary transport layer, JSON for message formatting, and JSON-RPC 2.0 to manage remote procedure calls. Additionally, it incorporates Server-Sent Events (SSE) for real-time streaming.  

By aligning with established web standards, A2A minimizes integration friction. Developers don’t need to learn new protocols or build custom middleware; standard web technologies suffice. This plug-and-play approach accelerates adoption and reduces development overhead in enterprise environments.

Vendor-Neutral and Framework-Agnostic

One of Agent2Agent’s standout strengths is its interoperability across ecosystems. It is intentionally designed to support agents regardless of their vendor origin or development framework. Whether built using Google’s Vertex AI Agent Development Kit (ADK) or open-source frameworks like LangChain, all agents adhering to A2A can interact seamlessly.  

This cross-compatibility prevents vendor lock-in and promotes flexibility. Organizations can combine best-of-breed agents from different providers without concern for underlying platform differences. For instance, a scheduling assistant from Vendor A can collaborate with a content generator from Vendor B, provided they both implement the A2A specification.

Rich, Structured Message Format

Agent2Agent reimagines agent communication by introducing a conversation-first model where every message adheres to a well-defined schema. Messages include metadata such as sender role (e.g., “user” or “agent”), a unique message ID, and one or more “parts” that hold the actual content.  

These parts aren’t limited to plain text; they can carry binary data, structured JSON, or even multimedia. This structure makes the conversation more expressive, machine-readable, and easier to debug. Developers can trace dialogue threads and analyze message content systematically, improving transparency and fault resolution.

Capability Discovery With Agent Cards

A2A introduces a lightweight yet powerful discovery mechanism through Agent Cards, JSON documents hosted at predictable locations (e.g., `/.well-known/agent.json`). Each Agent Card outlines the agent’s capabilities, accepted authentication methods, supported A2A versions, and endpoint details.  

This metadata acts like a digital profile or capability resume for the agent, allowing others to understand what it can do and how to interact with it. It supports automation in finding and engaging the right agent for the right task, much like a service registry in microservices.

Uniform Skill Invocation

Beyond discovery, A2A enables agents to invoke each other’s skills through standardized function calls. These “skills,” like “summarize a document” or “generate a chart,” are published as callable endpoints within the Agent Card.  

Because the function call format is consistent across agents, integration becomes modular. An agent needing translation, for example, can delegate that task to a language agent without hardcoding a custom API. This promotes a building-block approach, where agents serve as reusable service components in a larger system.

Threaded Conversations and Task Context

A2A excels in supporting multi-turn dialogues and long-lived task management. It uses unique identifiers for messages and their parent threads, which allows agents to maintain coherent conversations over time.  

Each task has its own lifecycle, tracked via a Task ID and a state machine (e.g., submitted, working, input required, completed). This structured context model ensures agents can handle follow-up questions, request clarification, or wait for additional input without losing the thread of the conversation.

Designed for Long-Running Tasks

Not all tasks can be completed in a few seconds. Agent2Agent is designed to handle asynchronous workflows and delayed results gracefully.  

Using Server-Sent Events, a remote agent can stream updates back to the client in real time, whether it’s showing progress, requesting input, or pushing partial results. Additionally, push notifications are supported via webhooks, allowing the client to be notified immediately when key milestones or events occur. This makes A2A well-suited for use cases like document review, procurement workflows, or AI-assisted research that may unfold over hours or days.

Enterprise-Grade Security

Security is embedded into A2A from the ground up. The protocol supports authentication and authorization schemes on par with OpenAPI, such as API keys, OAuth 2.0 tokens, and service accounts.  

All communication happens over HTTPS, ensuring confidentiality and data integrity. Agents can also enforce granular access controls, determining who can access which functions and under what conditions. This zero-trust, secure-by-default architecture enables A2A to operate safely within enterprise environments, even when sensitive systems are involved.

Modality-Agnostic Messaging

Unlike text-only protocols, A2A embraces multi-modal content exchange. Through its “parts” system, agents can include not just text, but also images, audio snippets, video streams, structured data, or even UI-rich payloads within messages.  

This ensures that agents with specialized capabilities, such as speech recognition, computer vision, or data visualization, can communicate naturally. The protocol is built to negotiate media formats, ensuring graceful fallbacks when agents cannot process certain types of content.

Predictable Error and Status Handling

Troubleshooting multi-agent interactions can be difficult when each agent has its own error style. A2A addresses this with a standardized error and status model.  

Responses follow consistent formats, with clearly defined status codes and messages. Agents can report errors like “invalid request” or “authorization failed” in a uniform way, similar to HTTP. This not only simplifies debugging but also improves the overall reliability and traceability of the system.

Conclusion

Google’s Agent2Agent (A2A) protocol marks an important milestone in the evolution of artificial intelligence. One of the biggest challenges today is that AI agents, software programs built to perform tasks autonomously, often struggle to work together if they’re created by different developers, written in different programming languages, or built on separate platforms. This lack of compatibility limits the potential of what AI systems can truly accomplish.

A2A solves this by providing a standardized, open protocol that allows different AI agents to communicate, collaborate, and share work, even if they come from completely different systems. This makes it possible to build multi-agent ecosystems, groups of AI agents that can interact and solve problems together in real time. Imagine one agent gathering data, another analyzing it, and a third presenting it in a human-friendly way, all working in sync without needing special custom integration.

What makes A2A even more promising is its open-source nature. This means developers, researchers, and organizations around the world can contribute to improving the protocol. With support from major tech players, the protocol is already gaining momentum and being integrated into real-world AI projects. It encourages innovation, flexibility, and interoperability across the board.

Agent2Agent is not just a technical upgrade; it’s a powerful enabler of the next generation of AI, where intelligent agents don’t work in isolation but as part of a larger, cooperative network. The future of collaborative AI is coming out fast, and A2A is helping lead the way.

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