In-depth Research Report on Google Agent2Agent (A2A) Protocol

✨Article Summary (AI Generated)

In April 2025, Google officially announced a new Agent2Agent (A2A) Protocol at the Cloud Next 2025 event. A2A is an open interoperability protocol designed to break down barriers between different AI agent frameworks and vendors, enabling secure and efficient cross-platform collaboration. Specifically designed for enterprise environments, this protocol addresses data silo issues by standardizing communication between agents, enabling the automation of complex workflows and boosting productivity.

Upon release, A2A received support from over 50 industry-leading enterprises (such as Atlassian, Box, Salesforce, SAP, ServiceNow, MongoDB, etc.). It provides a common framework for AI agents, allowing agents from different vendors or architectures to securely exchange information, coordinate actions, and integrate seamlessly into business operations.

This coalition reflects the industry's shared vision for AI agent interoperability, where in the future, regardless of the underlying technology, AI agents can "converse" and collaborate freely like network services.

Technical Principles

Core Architecture

An AI agent communication protocol based on HTTP+JSON, primarily consisting of the following modules:

1. Agent Card

   • Location: /.well-known/agent.json
   • Function: A JSON-formatted agent capability manual (including API endpoints, skills, authentication methods)

2. Role Model

   • Server: Provides API interfaces (requiring the implementation of standard methods such as tasks/send)
   • Client: Makes HTTP calls to other agents

3. Task Flow

   • Lifecycle: submitted → working → (input-required) → completed/failed
   • Interaction units:
     • Messages: Composed of multiple types of Parts (text/file/structured data)
     • Artifacts: Structured results produced by tasks

4. Communication Mechanism

   • Basic mode: HTTP + JSON-RPC style interfaces
   • Advanced features:
     • Real-time push: SSE protocol (tasks/sendSubscribe)
     • Asynchronous notifications: Webhook callbacks
     • Multimodal support: Achieved through the Part type

5. UX Negotiation

   • Pre-negotiation of interaction forms between agents (e.g., text/voice/forms)

Basic Framework and Dependencies

The A2A protocol itself does not rely on specific AI models or frameworks but is closely related to the trend of autonomous agents driven by large language models (LLM).

The protocol's structure draws upon the concepts of distributed systems and microservices, treating each AI agent as an independent service that communicates through standard interfaces. This means that any LLM-based agent development framework (such as LangChain, LangGraph, Google's Agent Developer Kit ADK, CrewAI, Genkit, etc.) can implement the A2A interface and thereby collaborate with other agents.

Google emphasizes that A2A is adaptable to "opaque" agents—those that do not need to expose internal reasoning processes or memory states. This is crucial for many enterprise-level applications since, in terms of security and compliance, agents tend to interact through clearly defined inputs and outputs rather than sharing internal details.

In summary, A2A provides an application layer protocol specification. Developers can build compliant agents using any technology stack (such as backend in Python, Node.js, etc.), as long as they adhere to the HTTP+JSON interface contract, enabling interconnectivity with other agents.

Protocol Significance: Design Goals and Multi-Agent Collaboration Value

Design Intent

The introduction of the Agent2Agent protocol stems from the real-world challenges enterprises face when deploying AI agents at scale. As various AI-powered agents (some for customer service, others for office automation, etc.) are introduced in different departments and teams, enabling communication and coordination between agents built by different vendors and frameworks to achieve more complex tasks becomes a significant challenge.

Traditionally, enterprises might need to custom-build integration interfaces or write "glue code" for each pair of interacting agents. This is not only costly but difficult to scale. The design goal of the A2A protocol is to establish a common language for heterogeneous AI agents, allowing them to collaborate out of the box without needing a custom integration layer for each combination. Drawing from its experience in deploying large-scale agent systems internally, Google has abstracted these common challenges, leading the way in forming the A2A as an open standard for the entire industry.

Openness and Neutrality

A2A is positioned as an open protocol standard rather than a proprietary Google interface, which is significant. Firstly, being open means that any organization or individual can implement their own A2A agents according to the publicly available specifications, and Google itself has open-sourced the protocol under the Apache 2.0 license on GitHub. More than 50 tech companies, SaaS providers, and consulting organizations participated in the protocol drafting from the start, contributing to its demands and standards.

This multi-party collaboration ensures that A2A has neutrality and universality, avoiding dominance by a single vendor. This is akin to the early days of the internet's HTTP protocol—widely recognized as an open standard fostering interconnectivity between different systems. Similarly, the A2A protocol is also hoped to become the "HTTP between agents," creating an open and unified communication layer for AI agents. Upon its introduction, it was viewed by some as a crucial signal towards AI agent interoperability standardization in the industry.

Complement to MCP

Notably, Google positions A2A as a complement rather than a replacement for the Model Context Protocol (MCP) proposed by Anthropic. The MCP protocol primarily addresses how a single agent uses tools and accesses external context, providing a consistent interface for AI models to call APIs and retrieve databases. In contrast, A2A focuses on dialogue and collaboration between agents.

Google metaphorically explains: "If MCP is a wrench that enables agents to use tools, then A2A is the dialogue between mechanics, allowing multiple agents to communicate like a team of mechanics diagnosing problems." In other words, A2A does not compete with MCP; rather, it serves its function—MCP empowers a single agent to connect external tools and data, while A2A connects multiple independent agents into a larger collaborative network. Together, they complement and strengthen a more robust autonomous agent ecosystem.

Enhancing Autonomy and Collaborative Efficiency

The value of the A2A protocol in multi-agent systems lies in increasing the ceiling of agent autonomy and collaboration. Through A2A, agents with different capabilities can dynamically form "digital workforce teams" to collaboratively handle complex tasks that a single agent cannot accomplish. For example, a sales AI assistant can directly request a financial AI agent to generate a quote; a customer service chatbot can communicate with a warehouse AI agent about inventory information.

This direct agent linkage saves on human coordination or extra intermediary layers, thereby improving task efficiency and success rate. Research indicates that multi-agent collaboration can significantly increase target completion rates compared to single-agent solutions in enterprise scenarios. Moreover, A2A's standardized approach lowers the marginal cost of introducing new agents—enterprises can add specialized agents into the workflow at any time, and they can immediately "converse" with existing agents without additional development, achieving plug-and-play extensibility.

Security and Governance

While emphasizing open collaboration, A2A also focuses on the security and governance concerns important to enterprises. The protocol's design features built-in authentication and authorization mechanisms, with the Agent Card explicitly listing the identity authentication methods required for agent services. Enterprises can control which agents have permission to communicate with each other and exchange which data through unified policy management.

This standardized management approach is more conducive to audit and risk control than having each interface operate independently. Additionally, since A2A only requires sharing the inputs and outputs needed for tasks without involving the internal reasoning process of agents, enterprises can have agents collaborate while protecting proprietary algorithms and data. In the long run, as more enterprises adopt A2A, they can also gain a unified perspective for cross-platform agent management, enabling monitoring and optimization of their entire AI agent "fleet." These standardized, controllable, and manageable features are critical to A2A's implementation in enterprise environments.

Application Scenarios: Automation, Multi-Agent Collaboration, Intelligent Assistants, and Enterprise Communication

As a bridge for multi-agent collaboration, the A2A protocol has extensive application potential across numerous business and product scenarios. Below are some typical scenarios and examples to demonstrate the value of A2A:

• Enterprise Process Automation
  In large enterprises, daily business processes often involve multiple systems and roles. Through A2A, agents with different functions can collaboratively complete end-to-end automated processes. For example, in IT operations scenarios, an agent responsible for asset management can request another agent in charge of procurement to automatically complete tasks such as "ordering a laptop for a new employee."

• Multi-Agent Collaboration
  A2A's most direct application is enabling multiple AI agents to form collaboration teams to divide and complete complex tasks. In the human resources recruitment process, enterprises may deploy different agents: one responsible for screening resumes, another for scheduling interview times, and another for answering candidates' common questions. Through A2A, these agents can notify each other of progress and share information.

• Intelligent and Personal Assistants
  Modern intelligent virtual assistants, for personal or workplace use, are becoming increasingly complex. Through the A2A protocol, the intelligent assistant no longer needs to be a single AI model but can be an integrated system that calls on a group of specialized agents behind the scenes. For instance, a personal intelligent assistant planning overseas travel for a user can call a "flight booking agent" to acquire flight options and then call a "trip optimization agent" to plan the travel itinerary, even collaborating with a "translation agent" for local language assistance.

• Enterprise Communication and Collaborative Work
  In large organizations, different departments may use their AI tools to enhance efficiency. Without a unified standard, these agents operate independently, making it difficult to share information. The A2A protocol allows enterprises to achieve cross-departmental agent collaboration; for example, a sales AI finding a technical issue with a customer can directly notify a customer service AI to follow up through A2A.

• Other Scenarios
  Beyond the aforementioned typical applications, A2A can also be used in customer service, e-commerce logistics, financial risk control, and many other fields. As AI agents are applied more deeply across industries, any scenario requiring multiple intelligent agents to collaborate can become a point of application for A2A.

Industry Impact: Ecosystem, Competitive Comparison, and Industry Trends

Ecosystem Construction

The introduction of the A2A protocol has already shown a tangible boosting effect on the AI development ecosystem. First, the involvement of numerous partners indicates that major industry players are working together to forge a unified agent communication standard.

For developers, this means that in the future, when building AI applications, they will be able to conveniently integrate intelligent components from different vendors without worrying about compatibility issues.

Impact on Competitive Landscape

As the first open multi-agent communication protocol standard, A2A also places pressure and influence on major AI platform providers. On one hand, Google, by leveraging the open standard to rally industry resources, has solidified its leadership in enterprise AI; on the other hand, other giants like Microsoft, OpenAI, IBM, etc., if they lack a similar standard, will need to consider whether to support A2A to be part of this ecosystem.

In the future, customers may demand AI products to have A2A compatibility to protect their investments. This will prompt more vendors to join the support line to avoid being excluded from the ecosystem.

Meaning for Developers and Enterprises

From a developer's perspective, the advent of A2A greatly simplifies the development of multi-agent applications. Previously, achieving interactions between two AI systems using different frameworks required developers to master both interfaces and write adaptation layers; now, as long as both sides implement the A2A interface, they can directly call each other.

This reduces the operational costs and technical risks of deploying AI agents in enterprises, encouraging more traditional IT departments to attempt introducing AI agent collaboration to improve business processes.

Industry Trend Outlook

The birth of the A2A protocol aligns with the trend of AI industry moving towards more autonomous and collaborative development. On one hand, the enhanced capabilities of large language models have spawned many applications for autonomous agents, and singular intelligence is gradually unable to meet complex business demands, pushing the architecture from "single agent + tool" to "multi-agent collaboration."

A2A provides the foundational standard necessary for this evolution. On the other hand, enterprises are increasingly demanding data sovereignty and system control—they wish to freely purchase the most suitable AI modules rather than being locked into a single vendor ecosystem. A2A, by allowing open interconnection, meets this decentralized and diversified demand, aligning with the industry's broad trend of open cooperation.

Related Materials: Papers, Blogs, Demos, and Code Repositories

Official Releases and Technical Blogs

Upon releasing the A2A protocol, the Google team provided comprehensive official documentation and blog posts. The most significant one is "Announcing the Agent2Agent Protocol (A2A)" published on the Google Developers Blog on April 9, 2025.

Technical Documentation and Specifications

The complete technical specifications of the A2A protocol have been open-sourced and released on a GitHub repository, which includes the protocol's JSON interface definitions, example codes, and documentation.

Open Source Code Repositories and SDKs

Besides specification documentation, the A2A GitHub repository contains sample implementations and development toolkits in multiple programming languages. Developers are welcome to test, reference, and contribute improvements.

Demos and Video Materials

To facilitate understanding of A2A's actual effects, Google provides several demo cases. For instance, scenarios demonstrating cross-platform agent collaboration were presented at the launch.

Community Discussions and Analytical Articles

The release of A2A has triggered extensive discussions in the AI technical community. Many industry analysts have published insights in blogs and media, providing different perspectives on the significance, application prospects, and potential challenges of the A2A protocol.

Are there any Academic Papers?

As of now (April 2025), the A2A protocol is mainly published industrially with no dedicated academic papers detailing its features. However, its concept aligns with research directions in multi-agent systems and distributed AI.

Conclusion

The Agent2Agent (A2A) protocol, as an open standard launched by Google in collaboration with industry players, heralds an era of direct dialogue and cooperation between AI agents. Its clear technical framework and enterprise-focused design fill the gap of lacking a universal communication standard in multi-agent systems.

A2A transitions AI agents from isolation to networks: agents with different capabilities can discover, communicate, and coordinate with each other like internet services, achieving autonomous intelligence on a broader scale. For enterprises, it introduces a new paradigm of cross-application AI automation, heralding increased efficiency and innovative possibilities; for developers, it provides standard tools and an ecosystem, significantly lowering the barrier for multi-agent development.

References

1. Rao Surapaneni et al., Announcing the Agent2Agent Protocol (A2A)
2. Kevin Ichhpurani, Building the industry’s best agentic AI ecosystem with partners
3. Google Open Source Project, Agent2Agent Protocol – README
4. Chris McKay, Google just Launched Agent2Agent... (Maginative, Apr 2025)
5. Ofweek VicoNet, Google Releases New Agent Protocol A2A... (Reposted from Sina Finance)
6. cnBeta News, Google Launches Agent2Agent Protocol for Cross-Platform AI Agent Connectivity