Google's A2A Protocol

Enabling Seamless AI Agent Collaboration

Solving the Challenge of AI Fragmentation

The proliferation of specialized AI agents has underscored a critical challenge: enabling seamless communication across diverse platforms and vendors. A month ago, in our article "Bridging AI Islands," we argued that a protocol like OVON (Open Voice Network) could serve as an essential agent-to-agent communication layer to complement standards like Anthropic’s Model Context Protocol (MCP), which enhances individual agent capabilities. At the time, OVON’s focus on voice-based interactions hinted at the potential for broader interoperability, though it faced visibility and adoption challenges. Since then, Google’s A2A (Agent-to-Agent) protocol, launched today (April 10, 2025), has emerged as a transformative development in this space.

A2A goes beyond early efforts like OVON, offering a modality-agnostic framework that enables AI agents to collaborate securely and efficiently, regardless of their underlying systems. With backing from over 50 industry partners, A2A addresses the growing fragmentation of AI ecosystems by standardizing agent-to-agent interactions. While it builds on the vision of interoperability we explored previously, A2A also complements MCP by focusing on communication rather than individual agent tooling – though, as we’ll discuss, this relationship isn’t without nuances. In this article, we explore A2A’s architecture, its industry momentum, and how it positions itself relative to MCP, OVON, and other standards like AGNTCY.

What is A2A?

The A2A protocol is designed to bridge the gap between specialized AI agents that typically operate in isolation due to differing frameworks or vendor ecosystems. As organizations adopt multiple AI systems—each tailored to a specific function—users often face the burden of manually switching between platforms or integrating them through custom solutions. A2A eliminates this friction by enabling agents to exchange data, coordinate tasks, and collaborate securely without human intervention.

This interoperability is critical in today’s AI landscape, where specialization is a strength but fragmentation is a bottleneck. For example, a customer service agent might need to consult a billing agent or a logistics agent to resolve an issue. A2A makes such interactions seamless, allowing agents to work together as a cohesive unit. Its significance lies in its potential to transform disjointed AI deployments into a unified, efficient system, enhancing both enterprise workflows and user experiences.

Technical Overview of A2A

A2A is built on familiar web technologies—HTTP, JSON-RPC, and Server-Sent Events (SSE)—making it easy to integrate into existing IT infrastructure. Its architecture follows a client-server model with a focus on task-oriented communication, supported by the following key components:

• Agent Card: A metadata file (typically located at /.well-known/agent.json) that describes an agent’s capabilities, endpoint URL, and authentication requirements. This allows other agents to discover and assess its suitability for a task.

• A2A Server: The agent receiving requests via an HTTP endpoint, responsible for processing tasks and delivering results or updates.

• A2A Client: An application or another agent that initiates communication and consumes A2A services. It sends requests (like tasks/send ) to an A2A Server's URL.

• Task: The central unit of work. A client initiates a task by sending a message. Tasks have unique IDs and progress through states ( submitted , working, input-required, completed, failed , canceled ).

• Message: Represents communication turns between the client ( role: "user" ) and the agent ( role: "agent" ). Messages contain Parts .

• Part: The fundamental content unit within a Message or Artifact . Can be

  TextPart , FilePart (with inline bytes or a URI), or DataPart (for structured JSON, e.g., forms).

• Artifact: Represents outputs generated by the agent during a task (e.g., generated

  files, final structured data). Artifacts also contain Parts.

The protocol organizes interactions around tasks, which follow a defined lifecycle:

1. Discovery: The client retrieves the server’s Agent Card to verify its capabilities.

2. Initiation: The client submits a task request with a unique Task ID.

3. Processing: The server executes the task, providing real-time updates (often via SSE) or synchronous results.

4. Interaction: If additional input is needed, the server requests it, and the client responds within the same task context.

5. Completion: The task concludes in a terminal state—completed, failed, or canceled—with results delivered.

Communication occurs through messages containing parts (e.g., text, files, or structured JSON data), enabling flexible and rich interactions. Security is a priority, with support for enterprise-grade authentication like API keys and OAuth 2.0.

Core Design Principles

A2A’s design is guided by five principles:

1. Embrace Agentic Capabilities: Agents collaborate using their natural modalities (e.g., text, audio, video) without requiring shared tools or memory.

2. Build on Existing Standards: Leveraging HTTP, SSE, and JSON-RPC ensures compatibility with modern systems.

3. Secure by Default: Robust authentication and authorization align with enterprise needs.

4. Support for Long-Running Tasks: A2A handles both quick exchanges and prolonged processes, offering real-time feedback.

5. Modality Agnostic: It supports diverse formats, from text to multimedia, broadening its applicability.

Advanced Features

A2A includes several sophisticated capabilities:

• Capability Discovery: Agents advertise their skills via the Agent Card, enabling precise task delegation.

• Task Management: A clear lifecycle (e.g., submitted, working, completed) structures multi-agent workflows.

• User Experience Negotiation: Agents can tailor response formats (e.g., text, forms, images) to the client’s needs.

• Streaming and Push Notifications: SSE provides real-time updates for long tasks, while webhooks enable proactive notifications.

Industry Support

A2A debuted with backing from over 50 partners spanning enterprise software, AI platforms, and consulting services, underscoring its potential as a widely adopted standard. Notable collaborators include:

• Enterprise Software Providers: Salesforce (Einstein agents), SAP (Joule copilot), ServiceNow, and Atlassian (Rovo agents) are integrating A2A to connect their systems with external agents.

• AI and Development Platforms: Cohere and LangChain are adapting their frameworks to support A2A, easing agent development.

• Consulting Firms: Accenture, Deloitte, KPMG, and others are poised to guide enterprises in adopting A2A solutions.

Use Cases and Implementations

Real-world applications highlight A2A’s versatility:

• SAP’s Customer Dispute Resolution: A contact center agent uses SAP’s Joule to resolve disputes by collaborating with a Google agent accessing BigQuery data, streamlining the process without manual intervention.

• Atlassian’s Agent Collaboration: Rovo agents leverage A2A to coordinate tasks across teams, enhancing workplace collaboration.

• Hiring Process Automation: A hiring manager delegates candidate sourcing to an agent, which then works with other agents to identify, interview, and onboard candidates.

This broad support and practical demonstrations signal A2A’s readiness to address real enterprise needs.

Implications for Business and Technology

A2A’s ability to connect disparate AI agents offers tangible benefits:

• Interoperability: Agents from different vendors can collaborate, reducing integration complexity.

• Automation: Multi-system processes—like supply chain management or customer support—become more efficient.

• Innovation: A2A could spawn an agent marketplace, where specialized agents are easily deployed and integrated.

• User Efficiency: Users interact with a single interface while agents handle backend coordination.

Business Process Transformation

A2A enables automation of complex workflows:

• Hiring: An agent manages sourcing, interviews, and onboarding by collaborating with specialized peers.

• Customer Support: Agents resolve issues by accessing data across systems without user involvement.

• Supply Chain: Inventory, logistics, and procurement agents optimize operations in real time.

This reduces context switching for users and supports delegation to agent teams, with humans providing oversight as needed.

Impact on the Enterprise Software Landscape

A2A’s reliance on standard technologies facilitates integration with existing systems, driving several shifts:

• Legacy System Enhancement: Older platforms can adopt AI capabilities via A2A-compatible agents.

• Reduced Vendor Lock-in: Organizations can mix solutions from multiple vendors seamlessly.

• Accelerated AI Adoption: Lower technical barriers encourage broader AI use.

This could pivot competition toward specialized agents and ecosystems, rather than all-in-one platforms.

Emergence of an Agent Economy

A2A could catalyze the development of specialized agent marketplaces, where

businesses can discover and deploy agents with specific capabilities. This "agent

economy" would function similarly to app stores, but for AI agents that can seamlessly

collaborate with existing systems.

The protocol enables new business models centered around specialized AI capabilities:

1. Agent-as-a-Service: Companies can offer specialized agents that integrate with

   existing enterprise systems through A2A.

2. Agent Orchestration Platforms: New platforms may emerge to help businesses

   discover, deploy, and manage multi-agent workflows.

3. Specialized Agent Development: Developers can focus on creating highly

   specialized agents without needing to build comprehensive solutions.

A2A and Other Standards

A2A’s place in the AI interoperability landscape is best understood through its relationship with existing standards. It is designed to complement MCP, with some caveats, but it competes with or diverges from initiatives like OVON and AGNTCY, reflecting distinct focuses and approaches.

MCP and A2A: Complementary Protocols with Caveats

A2A and MCP share a vision of enhancing AI capabilities but target different aspects of the ecosystem, operating in a layered, mostly complementary manner.

• Focus Areas:

  • MCP (Model Context Protocol): Launched by Anthropic in November 2024, MCP focuses on individual agent capabilities, standardizing how a single agent connects to tools and data sources to perform tasks effectively.

  • A2A (Agent-to-Agent): Introduced by Google in April 2025, A2A targets agent-to-agent communication, enabling multiple agents to collaborate across platforms and vendors.

• Technical Layers:

  • MCP: Operates at the tool and data access layer, providing agents with standardized, secure context and resources.

  • A2A: Functions at the communication layer, facilitating direct agent interactions for tasks requiring coordination.

Google positions A2A as complementary to MCP, stating in its announcement: "A2A is an open protocol that complements Anthropic’s Model Context Protocol (MCP), which provides helpful tools and context to agents." As Teneo.ai notes, "MCP provides standardized, secure context for individual agents, while A2A enables seamless communication and collaboration between agents."

However, this complementarity has caveats: overlapping use cases – such as scenarios where context-sharing and communication needs blur – could lead to redundancy or integration challenges, which remain to be fully resolved as both standards mature. Agent vs. Tool: Can MCP servers expose full-fledged agents, not just simple tools? Some teams are already experimenting with and discussing this, even my own PR to expose OpenManus agent as an MCP server. The line between a sophisticated tool and a simple agent can be blurry.

A2A vs. OVON

OVON, an early contender in agent interoperability, differs significantly from A2A in scope and ambition:

• Modality:

  • OVON focuses narrowly on voice-based interactions, aiming to standardize communication for voice agents.

  • A2A is modality-agnostic, supporting text, audio, video, and more, making it a broader solution.

• Security:

  • A2A incorporates enterprise-grade security features, addressing the needs of complex, multi-vendor environments—a step beyond OVON’s initial scope.

• Momentum:

  • OVON, while pioneering, lacks the extensive industry backing that A2A enjoys, with over 50 major technology companies and service providers supporting its launch.

Rather than complementing OVON, A2A effectively supersedes it by offering a more versatile and widely adopted framework, rendering OVON’s voice-specific approach less relevant in the current landscape.

A2A vs. AGNTCY

AGNTCY, announced in March 2025 by a coalition including Cisco, LangChain, and others, pursues a different vision from A2A:

• Approach:

  • AGNTCY aims to build a comprehensive “Internet of Agents” infrastructure, encompassing a wide-ranging ecosystem of standards and services.

  • A2A focuses specifically on the communication protocol, prioritizing practical agent-to-agent interactions.

• Components:

  • AGNTCY includes an Agent Directory, Open Agent Schema Framework, and Agent Connect Protocol, creating a broader but more complex system.

  • A2A emphasizes core protocol features like capability discovery, task management, and collaboration, offering a leaner, more focused solution.

• Maturity:

  • AGNTCY remains in development, with its ambitious scope still taking shape.

  • A2A launched with working implementations and significant industry support, giving it an immediate edge.

A2A does not position itself as complementary to AGNTCY but as a distinct alternative, targeting rapid deployment and adoption over AGNTCY’s broader, still-evolving infrastructure goals.

Challenges and Future Outlook

Adoption Hurdles

Despite its promise, A2A faces several adoption challenges:

1. Competing Standards: Other agent interoperability standards like AGNTCY may

   compete for adoption.

2. Implementation Complexity: Organizations must invest in adapting existing

   systems to support A2A.

3. Production Readiness: As noted by Google, a production-ready version is still

   forthcoming later in 2025.

Security and Governance

The protocol raises important security and governance considerations:

1. Authentication and Authorization: While A2A supports enterprise-grade security, organizations must implement proper controls.

2. Data Privacy: Agent collaboration may involve sharing sensitive data, requiring

   careful privacy controls.

3. Audit and Compliance: Multi-agent workflows may be more difficult to audit for

   compliance purposes.

Future Evolution

Short-term Impact

In the short term, we can expect:

• Initial adoption by early movers, particularly Google Cloud customers

• Development of A2A-compatible agents by major software vendors

• Emergence of best practices for A2A implementation

Medium-term Impact

In the medium term:

• Broader adoption across industries as the protocol matures

• Emergence of agent marketplaces and orchestration platforms

• Potential convergence or clearer differentiation between competing standards

Long-term Vision

The long-term vision suggested by A2A is:

• A rich ecosystem of specialized, interoperable agents

• Seamless collaboration between agents across organizational boundaries

• New business models centered around agent capabilities and orchestration

Embracing A2A for a Unified AI Ecosystem

Google’s A2A protocol is a pivotal step toward unifying the fragmented world of specialized AI agents. By enabling secure, efficient collaboration across platforms, it enhances automation, reduces integration complexity, and fosters innovation. With robust industry support and compatibility with standards like MCP, A2A is poised to shape the future of agentic AI. While challenges like security and adoption remain, its potential to streamline enterprise workflows and create a collaborative AI ecosystem makes it a development worth watching.