Agent2Agent (A2A): A Revolution in AI Agent Communication and the Future of Connected Artificial Intelligence

Why do we need AI Agents to understand each other?

I. Introduction: Why do we need AI Agents to understand each other?

We are entering an era where artificial intelligence (AI) is no longer a monolithic concept but is evolving into an ecosystem of specialized AI agents. These agents, designed for specific tasks, hold the promise of revolutionizing complex workflows and dramatically boosting productivity across industries. From customer support to data analytics to supply chain management, the potential is immense.

However, as the number of these specialized tools grows, a critical issue arises: their ability to communicate and collaborate with one another. Often developed by different teams, on different platforms, or even by different vendors, these agents frequently lack a shared language, resulting in fragmentation. This lack of interoperability hinders broader AI adoption in the enterprise and forces organizations to invest in complex, custom-built (and often fragile) integrations. Without a standardized way for AI agents to talk to each other, we quickly reach the limits of what they can collectively achieve.

In my own practice/observations, I’ve seen the incredible potential of AI agents—along with the frustration when valuable tools cannot work together. Imagine constructing a complex machine in which every part speaks a different language. That’s the reality for many enterprise AI deployments today. This “digital Babel” impedes efficiency and stifles innovation. The growing “pain point” in enterprise AI—inefficiencies stemming from fragmented agent ecosystems—has created a strong demand for a solution. Businesses face high costs and complexity when trying to integrate these isolated agents, fueling a market push for a standardized approach.

In response to this critical challenge, the Agent2Agent (A2A) protocol has emerged. Led by Google and supported by a broad coalition of technology partners, its goal is to create that missing “common language” for AI agents and solve interoperability once and for all. A2A isn’t just a technical exercise; it’s a direct response to market needs, aimed at unlocking the next wave of AI adoption and value in the enterprise.

II. What is the Agent2Agent (A2A) protocol?

Agent2Agent (A2A) is an open protocol originally initiated and led by Google, with significant support and contributions from over 50 leading technology and consulting companies. These include giants like Atlassian, Box, Salesforce, SAP, and ServiceNow, as well as major service providers such as Accenture, BCG, and Deloitte. The emphasis on “open” is crucial—its purpose is to avoid vendor lock-in and encourage as widespread industry adoption as possible.

The core mission of A2A is to enable AI agents—regardless of the framework (e.g., LangChain, CrewAI, Google ADK, custom solutions) or vendor platform they’re built on—to communicate with each other, securely exchange information, and coordinate their actions. It provides a standardized method for agents to collaborate and function as a cohesive whole rather than standalone entities. Think of it as a “common language” that abstracts away the differences between various implementations.

The design of the A2A protocol is guided by several key principles:

1. Agent capabilities support: Focused on enabling natural, often unstructured collaboration between agents, even if they do not share memory, tools, or context.

2. Building on existing standards: Leveraging proven, widely adopted technologies such as HTTP, Server-Sent Events (SSE), and JSON-RPC to facilitate easier integration into existing IT infrastructures.

3. Security by default: Implementation of enterprise-grade authentication and secure communication mechanisms.

4. Support for long-running tasks: Ability to manage and track asynchronous operations that may take a significant amount of time.

5. Modality independence: Support for multiple forms of communication, including text, structured data, audio, and video.

Google’s major investment, along with that of dozens of prominent partners, indicates that A2A isn’t just a technical standard but a strategic initiative that aims to shape the future AI landscape. By creating a standardized interaction layer for agents, the hope is to build an ecosystem where early adopters—such as Google Cloud with Vertex AI—gain an edge by deeply integrating and championing the protocol. While the specification is open, creating a de facto standard backed by powerful players can yield network effects that reinforce the position of the originators and their platforms in the rapidly expanding AI agent market. The likely goal is to foster an ecosystem in which Google Cloud and its partners play a central role.

III. Why is A2A important for the future of AI?

The significance of the A2A protocol lies in its ability to address some of the most pressing problems in enterprise AI today and open up new possibilities:

1. Breaking down enterprise silos: One of the biggest hurdles is the fragmentation of data and processes across different applications (CRM, ERP, support systems, etc.). A2A allows agents operating in these separate systems to communicate, hand off tasks, and share the data they need. Imagine a customer service chatbot that needs billing information from an ERP system. Through A2A, it can delegate that request to a specialized ERP agent and retrieve the answer without any complex, manual system integrations. In doing so, A2A helps overcome fragmentation and supports truly end-to-end automation across enterprise applications.

2. Driving innovation and flexibility (avoiding vendor lock-in): An open standard like A2A frees enterprises to choose the best specialized agents from various providers, mixing and matching them as needed rather than being tied to the ecosystem of a single vendor. This promotes healthy competition, pushes vendors to innovate, and allows organizations to flexibly assemble optimal AI solutions.

3. Enabling complex automation and sophisticated workflows: The real power of AI agents lies in their ability to work together on complex, multi-step tasks. A2A provides a framework for orchestrating such processes, where different specialized agents can collaborate—sequentially or in parallel—to achieve a complex objective. Examples include an automated hiring process (a candidate-sourcing agent working alongside an interview-scheduling agent and a background-check agent), advanced IT incident diagnosis and resolution, or intricate research projects. A2A makes it possible to realize “true multi-agent scenarios” and efficiently manage long-running tasks.

4. Ensuring future-proof AI investments: By building AI solutions with interoperability in mind—through standards like A2A—enterprises create systems that are more resilient to future changes and better able to adopt new technologies and frameworks. This helps reduce the risk of AI investments and makes them more adaptable in a rapidly evolving AI landscape. A2A could also serve as a foundation for advanced research on agent collaboration.

The real value of A2A goes beyond connecting Agent A with Agent B. It’s about enabling new, emergent capabilities that arise from multi-agent cooperation. The whole becomes more than the sum of its parts. Because agents can delegate tasks and coordinate actions within complex workflows that would be too large or intricate for a single agent to handle, there’s a multiplying effect on productivity and efficiency. A2A is the crucial piece that unlocks and magnifies these benefits of AI automation.

IV. How does A2A work (In simplified terms)?

A2A defines a structured way for agents to interact. Key mechanisms include:

1. Discovery via “Agent Cards”: Any agent that wants to be discoverable publishes its “Agent Card”—a public metadata file (commonly in JSON format, accessible at a standard path /.well-known/agent.json). This card describes the agent’s capabilities (what it can do), its skill sets, the network address (endpoint URL) where it can be reached, and authentication requirements. This allows other agents (“clients”) to dynamically search and identify suitable (“remote” or “server”) agents for a given task.

2. Task-oriented communication: Interactions between agents revolve around a “Task,” the central unit of work. A client initiates a task by sending a request to the server agent. Each task has a unique identifier and goes through defined states (e.g., submitted, working, input-required, completed, failed, canceled). This structure provides clarity in tracking progress, particularly for asynchronous or long-running operations.

3. Messages and artifacts: Actual communication happens through the exchange of “Messages.” Messages contain discrete “Parts,” each of which can carry different types of content—text (TextPart), files (FilePart, either embedded or as a URI), or structured data in JSON (DataPart), such as forms. The outputs or results of an agent’s work are referred to as “Artifacts,” which also contain Parts. The protocol supports multiple data types, including multimedia.

4. Interaction modes: A2A supports various interaction patterns depending on the nature of the task. Beyond a simple request-response model, it also allows real-time streaming updates (via Server-Sent Events, SSE) for monitoring long-running tasks and push notifications (via webhooks) to proactively inform the client of a task’s status changes.

5. Built on web standards: As noted, A2A deliberately uses familiar, battle-tested web technologies like HTTP, JSON, and SSE. This makes it much easier to integrate into existing IT infrastructures and lowers barriers to adoption.

The technical choices in A2A—Agent Cards for discovery, a defined Task lifecycle for managing interactions, reliance on standard web protocols—show a design focused on practical implementation in real-world enterprise environments. Priority was given to ease of adoption and integration over creating entirely new, untested paradigms. By leveraging established technologies, the learning curve for developers and IT teams is reduced. At the same time, elements like Agent Cards and Task states supply the structure needed to manage agent communication in more complex scenarios. This pragmatic approach is key to supporting real-world deployments and broader acceptance of the standard.

V. A2A vs. MCP: Two Different Tools

In discussions of AI agent standards, another protocol often arises: Model Context Protocol (MCP) by Anthropic. It’s important to understand how A2A relates to MCP so as to avoid confusion.

Google officially positions A2A and MCP as complementary protocols addressing different layers of agentic systems. Each focuses on a different aspect of agent interactions:

• MCP (Model Context Protocol): Its main purpose is to standardize how context, tools, and data are provided to a single agent or LLM so it can effectively perform its task. You might think of MCP as the standard for “packaging” input information and defining the interface for accessing external tools (APIs) that the agent uses for decision-making and actions.

• A2A (Agent2Agent Protocol): Focuses on communication and coordination between independent agents. It defines how agents discover each other, how they hand off tasks, how they share results, and how they track the state of their collaboration. Essentially, it’s a protocol for orchestration in multi-agent systems.

A handy analogy from a recent article: in an auto repair shop, MCP is the protocol connecting mechanics (agents) with their tools (e.g., “raise the lift by two meters,” “turn the wrench four millimeters to the right”). A2A is the protocol that enables communication among the mechanics themselves (e.g., the mechanic speaks with the parts supplier’s agent) and with the customer (“my car makes a rattling sound,” “please send me a photo of the left wheel”).

While Google publicly endorses MCP, it also actively champions A2A as the go-to standard for inter-agent coordination. The fact that some major players—such as Anthropic—are missing from the A2A partner list could hint at future friction.

This approach—supporting MCP while simultaneously pushing A2A—can be seen as strategic co-opetition on Google’s part. By acknowledging MCP, Google avoids alienating portions of the community (including OpenAI) that have already embraced MCP. At the same time, A2A lets Google define and steer standardization in the inter-agent coordination space, which is potentially more complex and extremely valuable for enterprise orchestration. In doing so, Google can carve out a strong position in the broader AI infrastructure without going head-to-head with MCP in its primary domain.

VI. Potential and Challenges

The A2A protocol offers tremendous potential but also faces significant challenges.

Potential:

1. Transformation of workflows: As noted, seamless multi-agent collaboration across platforms can radically improve complex processes in areas such as recruiting, customer support, research, IT incident management, supply chain operations, and more.

2. Accelerated AI value: By removing interoperability hurdles, enterprises can more quickly deploy and scale AI solutions, leading to faster ROI and tangible business outcomes.

3. New ecosystem development: A2A could foster the emergence of “agent marketplaces” or interconnected ecosystems where specialized agents from different providers can be easily discovered, integrated, and combined to deliver new, innovative services.

Challenges:

1. Adoption: The success of any standard hinges on widespread adoption. A2A will need buy-in from developers, vendors, and enterprises beyond the initial circle of partners. Will the community rally around this protocol?

2. Standards conflicts: There’s a risk of ongoing fragmentation or practical conflicts with other protocols like MCP if the industry doesn’t converge on clear consensus and scope boundaries.

3. Security and governance: Even though A2A is designed with security in mind, implementing and governing secure enterprise-level communications among agents at scale will be challenging. Robust frameworks for access control, auditing, and regulatory compliance must be developed.

4. Complexity management: Orchestrating and troubleshooting intricate multi-agent systems can be difficult even with a standard protocol. New tools and best practices will be needed to administer such systems effectively.

5. Maturity: A2A is relatively new (announced in April 2025, with production-ready releases projected for later in the year). The protocol itself, along with supporting tools, libraries, and best practices, will require time to mature and stabilize.

While the vision of A2A is compelling, its ultimate success won’t depend solely on a high-quality technical specification. Execution is critical: driving adoption, building an active community, ensuring robust and trusted security implementations, and most importantly demonstrating tangible value in real, complex enterprise deployments. The gap between an open spec and a universally accepted, reliably implemented standard can be substantial. Success demands not only technical excellence but also an effective ecosystem-building strategy, strong documentation, accessible tooling, and market trust.

VII. Conclusion: Toward a more connected AI

Agent2Agent (A2A) is a significant and ambitious initiative that may become a key element of the infrastructure for the next generation of artificial intelligence. By focusing on a fundamental issue—interoperability between AI agents—A2A lays the groundwork for a future in which AI systems can collaborate much more effectively and autonomously.

Looking ahead, protocols like A2A go beyond mere “plumbing.” They are catalysts for a future where AI is integrated deeper into our processes, capable of acting more autonomously, and collectively tackling complex challenges across multiple domains and industries. While challenges around adoption, standardization, and security remain, moving toward standardized communication is an essential step toward realizing AI’s vast potential.

Development of A2A is happening right now and is open to the community. What possibilities does seamless agent collaboration open up in your field or industry? How do you see standards like A2A shaping the way we design, build, and deploy AI systems? Feel free to share your thoughts in the comments.

The future of AI will likely belong to connected systems. Ensuring success in this vision depends not only on cooperation among AI agents themselves but also on collaboration within the technology community to build open, robust, and trusted standards that make this connected future possible.

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