Agent2Agent (A2A): Unlocking AI Collaboration

The Genesis of A2A: Addressing the Challenges of AI Integration

To fully appreciate the significance of A2A, it’s essential to understand the context that led to its creation. The rise of powerful language models like GPT-3.5 marked a turning point in AI adoption, as developers sought ways to extend their capabilities beyond simple chat interfaces.

One early solution was function calling, which allowed Large Language Models (LLMs) to connect with external APIs on a one-to-one basis. However, this approach quickly led to a fragmented ecosystem, where different AI vendors and implementers adopted varying integration methods, resulting in limited interoperability. This presented significant hurdles for building complex AI systems that required interaction between multiple AI entities. The lack of a standardized communication protocol meant that developers had to create custom solutions for each agent pairing, leading to increased development costs and complexity.

Anthropic’s Model Context Protocol (MCP) emerged as a potential solution to the ‘NxM problem,’ where the number of agents/AI systems (N) is multiplied by the number of tools/data sources (M). MCP aimed to standardize context and simplify integration, but Google recognized the need for a protocol that would enable agents to communicate directly with each other, creating a more dynamic and collaborative AI environment. MCP focused on the interaction between agents and external resources, but it didn’t directly address the need for agents to communicate and coordinate amongst themselves.

This is where A2A comes in. Like MCP, A2A unifies how AI agents interact, but instead of focusing on connecting agents to tools and data, it focuses on connecting agents to other agents. It’s a crucial step towards building truly collaborative AI systems, fostering a more interconnected and efficient AI landscape. A2A aims to create a common ground for AI agents, allowing them to share information, delegate tasks, and collaborate on complex projects without the need for complex custom integrations. The goal is to unlock the full potential of AI by enabling seamless teamwork between different AI entities.

Unveiling the Essence of A2A: A Universal Language for AI Agents

A2A is an open protocol that empowers AI agents to communicate with each other, irrespective of their origin or design. It acts as a translator, understanding and interpreting various languages and frameworks, such as LangChain, AutoGen, and LlamaIndex. By supporting these popular frameworks, A2A ensures broad compatibility and ease of integration for developers working with different AI tools and technologies.

Launched in April 2025, A2A was developed in collaboration with over 50 technology partners, including industry giants like Atlassian, Salesforce, SAP, and MongoDB. This collaborative approach ensures that A2A is not just a Google initiative but a broader industry effort towards standardization, promoting widespread adoption and fostering a vibrant ecosystem around the protocol. The involvement of numerous industry leaders demonstrates the importance of A2A in shaping the future of AI collaboration and highlights the shared commitment to creating a more interconnected and interoperable AI world.

At its heart, A2A treats each AI agent as a networked service with a standard interface. This is analogous to how web browsers and servers communicate using HTTP, but instead of websites, it’s for AI agents. Just as MCP addresses the NxM problem, A2A simplifies the process of connecting different agents without requiring custom code for each pairing. This standardized interface allows agents to easily discover and interact with each other, regardless of their underlying architecture or implementation. The HTTP analogy underscores the simplicity and universality of A2A, making it easy to understand and adopt for developers familiar with web technologies.

Deciphering the Core Capabilities of A2A: Enabling Seamless Collaboration

A2A is built upon four key capabilities that make agent collaboration a reality. To understand these capabilities, it’s important to define a few key terms:

• Client agent/A2A client: The app or agent that consumes A2A services. This is the ‘main’ agent that initiates tasks and communicates with other agents.
• Remote agent/A2A server: An agent that exposes an HTTP endpoint using the A2A protocol. These are the supplementary agents that handle task completion.

With these definitions in mind, let’s explore the four core capabilities of A2A:

1. Capability Discovery: This capability answers the question, ‘What can you do?’ It allows agents to advertise their capabilities through ‘Agent Cards,’ which are JSON files that provide a machine-readable profile of the agent’s skills and services. This helps client agents identify the best remote agent for a specific task, ensuring that tasks are delegated to the most qualified agent for optimal performance. The Agent Cards act as a directory of AI agents, allowing clients to easily find and connect with the agents that best meet their needs.
2. Task Management: This capability addresses the question, ‘Is everyone working together, and what’s your status?’ It ensures that communication between client and remote agents is focused on task completion, with a specific task object and lifecycle. For long-running tasks, agents can communicate to stay in sync, providing real-time updates and ensuring that all agents are aware of the task’s progress. This capability promotes efficient workflow management and helps to prevent conflicts or delays in task completion.
3. Collaboration: This capability focuses on the question, ‘What’s the context, reply, task output (artifacts), or user instruction?’ It enables agents to send messages back and forth, creating a conversational flow. This allows agents to exchange information, ask questions, and provide feedback, fostering a more collaborative and interactive environment. The conversational flow ensures that agents can effectively communicate and coordinate their efforts to achieve common goals.
4. User Experience Negotiation: This capability addresses the question, ‘How should I show content to the user?’ Each message contains ‘parts’ with specific content types, allowing agents to negotiate the correct format and understand UI capabilities like iframes, video, and web forms. Agents adapt how they present information based on what the receiving agent (client) can handle, ensuring that the user experience is optimized for the specific device or platform being used. This capability is crucial for creating a seamless and user-friendly AI experience, regardless of the underlying technology.

Demystifying the Inner Workings of A2A: A Client-Server Model for AI Communication

A2A operates on a client-server model, where agents communicate over standard web protocols like HTTP using structured JSON messages. This approach ensures compatibility with existing infrastructure while standardizing agent communication. The use of standard web protocols makes A2A easy to integrate with existing systems and reduces the need for custom development. The structured JSON messages provide a clear and consistent format for agent communication, ensuring that messages are easily understood and processed by both client and server agents.

To understand how A2A achieves its goals, let’s break down the core components of the protocol and explore the concept of ‘opaque’ agents. Understanding these core components is crucial for developers who want to implement A2A in their own AI systems.

Core Components of A2A: Building Blocks for AI Collaboration

• Agent Card: This JSON file, typically hosted at a well-known URL (e.g., /.well-known/agent.json), describes an agent’s capabilities, skills, endpoint URL, and authentication requirements. It serves as an agent’s machine-readable ‘résumé,’ helping other agents determine whether to engage with it. The Agent Card is the first point of contact between agents, providing essential information about an agent’s capabilities and how to interact with it.
• A2A Server: An agent that exposes HTTP endpoints using the A2A protocol. This is the ‘remote agent’ in A2A, which receives requests from the client agent and handles tasks. Servers advertise their capabilities via Agent Cards. The A2A Server is responsible for processing requests from client agents and providing the requested services.
• A2A Client: The app or AI system that consumes A2A services. The client constructs tasks and distributes them to the appropriate servers based on their capabilities and skills. This is the ‘client agent’ in A2A, which orchestrates workflows with specialized servers. The A2A Client is responsible for initiating tasks and coordinating the work of multiple A2A Servers.
• Task: The central unit of work in A2A. Each task has a unique ID and progresses through defined states (e.g., submitted, working, completed). Tasks serve as containers for the work being requested and executed. The Task object provides a structured way to manage and track the progress of a specific unit of work.
• Message: A communication exchange between the client and the agent. Messages are exchanged within the context of a task and contain Parts that deliver content. Messages are the primary means of communication between client and server agents, allowing them to exchange information, instructions, and feedback.
• Part: The fundamental content unit within a Message or Artifact. Parts can be:
  • TextPart: For plain text or formatted content
  • FilePart: For binary data (with inline bytes or a URI reference)
  • DataPart: For structured JSON data (like forms)
• Artifact: The output generated by an agent during a task. Artifacts also contain Parts and represent the final deliverable from the server back to the client. Artifacts represent the results of a task, providing the client agent with the information it needs to complete its workflow.

The Concept of Opaque Agents: Protecting Intellectual Property and Ensuring Security

The term ‘opaque’ in the context of A2A signifies that agents can collaborate on tasks without revealing their internal logic. This means that:

• An agent only needs to expose what tasks it can perform, not how it performs them.
• Proprietary algorithms or data can remain private.
• Agents can be swapped out with alternative implementations as long as they support the same capabilities.
• Organizations can integrate third-party agents without security concerns.

A2A’s approach simplifies the development of complex, multi-agent systems while maintaining high security standards and safeguarding trade secrets. The opaque nature of agents allows organizations to protect their intellectual property while still participating in the A2A ecosystem. This is crucial for fostering trust and collaboration among different AI developers and organizations.

A Typical A2A Interaction Flow: A Step-by-Step Guide

When agents communicate via A2A, they follow a structured sequence:

1. Discovery Phase: Imagine a user asking their main AI agent, ‘Can you help me plan a business trip to Tokyo next month?’ The AI recognizes the need to find specialized agents for flights, hotels, and local activities. The client agent identifies remote agents that can assist with each task and retrieves their Agent Cards to assess their suitability. This phase involves identifying the appropriate agents for each task and gathering information about their capabilities.
2. Task Initiation: With the team assembled, it’s time to assign jobs. The client agent might say to the travel booking agent, ‘Find flights to Tokyo from May 15th to the 20th.’ The client sends a request to the server’s endpoint (typically a POST to /taskssend), creating a new task with a unique ID. This includes the initial message detailing what the client wants the server to do. This phase involves creating a Task object and sending it to the appropriate A2A Server.
3. Processing: The booking specialist agent (server/remote agent) begins searching for available flights matching the criteria. It might:
   • Complete the task immediately and return an artifact: ‘Here are the available flights.’
   • Request more information (setting the state to input-required): ‘Do you prefer a specific airline?’
   • Begin working on a long-running task (setting state to working): ‘I’m comparing rates to find you the best deal.’ This phase involves the A2A Server processing the Task and returning a result or requesting more information.
4. Multi-Turn Conversations: If more information is needed, the client andserver exchange additional messages. The server might ask clarifying questions (‘Are connections okay?’), and the client responds (‘No, direct flights only.’), all within the context of the same task ID. This phase involves exchanging messages between the client and server agents to clarify requirements and provide additional information.
5. Status Updates: For tasks that take time to complete, A2A supports several notification mechanisms:
   • Polling: The client periodically checks the task status.
   • Server-Sent Events (SSE): The server streams real-time updates if the client is subscribed.
   • Push notifications: The server can POST updates to a callback URL if provided. This phase ensures that the client agent is kept informed of the progress of the Task.
6. Task Completion: When finished, the server marks the task as completed and returns an artifact containing the results. Alternatively, it might mark the task as failed if it encountered problems, or canceled if the task was terminated. This phase involves the A2A Server returning the results of the Task to the client agent.

Throughout this process, the main agent might simultaneously work with other specialist agents: a hotel expert, a local transportation guru, an activity mastermind. The main agent will create an itinerary by combining all these results into a comprehensive travel plan, then present it to the user. This demonstrates the power of A2A to enable complex workflows involving multiple AI agents.

In essence, A2A empowers multiple agents to contribute and collaborate towards a common goal, with a client agent assembling a result that surpasses the sum of its parts. This collaborative approach allows for more efficient and effective problem-solving, unlocking new possibilities for AI applications.

A2A vs. MCP: A Synergistic Partnership for AI Integration

While A2A and MCP might appear to compete for the same space, they are designed to work in tandem. They address distinct yet complementary aspects of AI integration:

• MCP connects LLMs (or agents) to tools and data sources (vertical integration).
• A2A connects agents to other agents (horizontal integration).

Google has deliberately positioned A2A as complementary to MCP. This design philosophy is evident in the launch of their Vertex AI agent builder with built-in MCP support alongside A2A. This demonstrates Google’s commitment to providing a comprehensive suite of tools for AI development and integration.

To illustrate this point, consider this analogy: If MCP enables agents to use tools, then A2A is their conversation while they work. MCP equips individual agents with capabilities, while A2A helps them coordinate those capabilities as a team. This analogy highlights the distinct but complementary roles of A2A and MCP in the AI ecosystem.

In a comprehensive setup, an agent might use MCP to retrieve information from a database and then use A2A to pass that information to another agent for analysis. The two protocols can work together to create more complete solutions for complex tasks, while simplifying the development challenges that have existed since LLMs became mainstream. The combination of A2A and MCP allows developers to build more sophisticated and powerful AI systems that can leverage the strengths of both protocols.

A2A Security Standards: Ensuring Enterprise-Grade Protection

A2A was developed with enterprise security as a primary concern. In addition to the exclusive use of opaque agents, each Agent Card specifies the required authentication method (API keys, OAuth, etc.), and all communications are designed to occur over HTTPS. This enables organizations to establish policies governing which agents can communicate with each other and what data they can share. These security measures are crucial for ensuring the privacy and security of sensitive data.

Similar to the MCP specification for authorization, A2A leverages existing web security standards rather than creating new modalities, ensuring immediate compatibility with current identity systems. The use of existing web security standards simplifies integration and reduces the risk of vulnerabilities.

Since all interactions occur through well-defined endpoints, observability becomes straightforward, allowing organizations to integrate their preferred monitoring tools and obtain a unified audit trail. This enhanced observability makes it easier to detect and respond to security threats.

A2A Ecosystem and Adoption: A Growing Community of Support

The A2A protocol has launched with significant support from over 50 technology partners, many of whom either currently support or intend to support A2A with their own agents. This widespread support demonstrates the importance of A2A in shaping the future of AI collaboration.

Google has integrated A2A into its Vertex AI platform and ADK, providing a simplified entry point for developers already within the Google Cloud ecosystem. This integration makes it easier for developers to get started with A2A and leverage its capabilities in their own AI projects.

Organizations considering A2A implementation should consider the following:

1. Reduced Integration Cost: Instead of building custom code for each agent pairing, developers can implement A2A universally, lowering integration costs. This is a significant benefit for organizations that need to integrate multiple AI agents.
2. Relatively Recent Release: A2A is still in its early stages of wide release, meaning it has yet to undergo the extensive real-world testing necessary to uncover potential shortcomings at scale. This is a factor to consider when evaluating the risks and benefits of adopting A2A.
3. Futureproofing: As an open protocol, A2A allows new and old agents alike to integrate into its ecosystem without requiring additional effort. This ensures that A2A remains relevant and adaptable as the AI landscape evolves.
4. Agent Limitations: While A2A represents a significant step forward for truly autonomous AI, it remains task-oriented and does not operate fully independently. This is a limitation to be aware of when designing AI systems using A2A.
5. Vendor Agnosticism: A2A does not lock organizations into any specific model, framework, or vendor, allowing them to mix and match across the entire AI landscape. This vendor agnosticism provides organizations with greater flexibility and control over their AI investments.

The Future of the Agent2Agent Protocol: A Vision for Seamless AI Collaboration

Looking ahead, A2A is expected to undergo further improvements, as outlined in the protocol’s roadmap. Planned enhancements include:

• Formalized authorization schemes and optional credentials directly within Agent Cards.
• Dynamic UX negotiation within ongoing tasks (such as adding audio/video mid-conversation).
• Improved streaming performance and push notification mechanics.

Perhaps the most exciting long-term possibility is that A2A will become for agent development what HTTP was for web communication: a catalyst for an explosion of innovation. As adoption increases, we may see pre-packaged ‘teams’ of agents specialized for particular industries, and eventually, a seamless global network of AI agents that clients can leverage. This vision highlights the transformative potential of A2A to revolutionize the way we interact with AI.

For developers and organizations exploring AI implementation, now is the ideal time to learn and build with A2A. Together, A2A and MCP represent the beginning of a more standardized, secure, and enterprise-ready approach to AI. By embracing these protocols, organizations can unlock new opportunities for innovation and create more powerful and effective AI solutions.