Google's A2A Protocol
Deep Research using ChatGPT o1-pro, by Rahul Parundekar. Published on 12th April, 2025.
Introduction
Google’s Agent-to-Agent (A2A) protocol is a new open standard designed to enable seamless communication and collaboration between autonomous software agents across different applications and organizations. Launched with contributions from over 50 technology partners, A2A establishes a common framework for AI agents to securely exchange information and coordinate actions on top of various enterprise platforms (Announcing the Agent2Agent Protocol (A2A) – Google Developers Blog). In essence, A2A allows independent “black-box” AI systems – potentially built by different teams or vendors – to work together as part of a unified workflow. This capability is critical as businesses increasingly deploy AI agents for complex tasks like customer support, workflow automation, and decision support. A2A’s open, vendor-neutral approach means an organization can combine the strengths of multiple AI services without the integration friction that traditionally comes with proprietary APIs. Tech executives recognize A2A as “a significant step beyond simple API integrations” in enabling cross-platform AI interactions. By standardizing how agents talk to each other, A2A empowers enterprises to break down data and functionality silos, accelerate automation, and orchestrate AI-driven processes that span departments and even companies. The result is an enterprise AI ecosystem that is more flexible, interoperable, and scalable, with agents from different vendors able to share context and collaborate to achieve business outcomes. This report provides a technical and strategic overview of A2A’s architecture, its role in enterprise AI “jobs to be done,” and its unique contributions to agent interoperability, task orchestration, and modular trust frameworks.
Architecture and Key Design Principles
At its core, A2A is an application-level protocol that defines how two agents communicate. The protocol was co-designed by Google and industry partners drawing on experience deploying large-scale multi-agent systems. Several foundational design principles guided A2A’s architecture:
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Agent-Centric Collaboration: A2A focuses on enabling rich agent-to-agent interactions without requiring agents to be treated as mere “tools” or share internal logic. Agents can collaborate in their natural, unstructured modalities (e.g. free-form text, images) even if they don’t share memory or internal tools. This embraces the unique capabilities of each agent while allowing true multi-agent scenarios beyond simple API calls. Notably, agents remain opaque to each other – they exchange information and requests without revealing proprietary reasoning or data unless explicitly shared, preserving modularity and IP. This opaque execution model means one agent can leverage another’s skills “without requiring [agents] to share internal tools or memory.”
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Built on Web Standards: To ease adoption, A2A is implemented on top of ubiquitous standards like HTTP(S) for transport, JSON-RPC 2.0 for request/response messaging, and Server-Sent Events (SSE) for streaming updates. By using well-known protocols, A2A can integrate with existing IT infrastructure (networking, proxies, logging) and developers can work with familiar patterns. The use of JSON-RPC (a lightweight remote procedure call format over JSON) provides a simple way to encode agent requests and responses, while SSE enables real-time push of information. In practice, an A2A interaction is just an HTTPS call carrying a JSON payload, making it straightforward to implement on any platform.
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Secure by Default (Enterprise-Grade Security): The protocol is designed to support enterprise-class authentication, authorization, and governance from day one. Each agent advertises its auth requirements and supported security schemes as part of its metadata (Agent Card), allowing flexible integration with API keys, tokens, or other credentials as needed. A2A’s security model has parity with common OpenAPI-style auth schemes used in enterprise APIs, meaning organizations can reuse existing identity and access management setups to control which agents can talk or what data can be shared. All agent-to-agent calls occur over TLS (HTTPS) and include whatever credentials or tokens the receiving agent requires. Because security is baked in at the protocol level, companies can enforce policy and trust boundaries in a standardized way. The open design also enables modular trust frameworks – for example, an enterprise could plug in different authentication modules or auditing hooks without changing the core protocol.
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Support for Long-Running Tasks and Async Interaction: Real-world business processes often involve lengthy operations or human-in-the-loop steps. A2A is designed to handle both short requests and multi-step dialogues or long-running jobs that may span minutes or hours (e.g. an agent researching a topic or waiting for a human approval). The protocol supports asynchronous patterns through a subscription model and callbacks. For instance, a client agent can initiate a task and stream progress updates from the remote agent via SSE events, or receive push notifications when sub-tasks complete. This means an orchestrator agent can fire off parallel tasks to various agents and get real-time status (“50% done”, “waiting on input”) and intermediate results, without blocking or polling. State management is built in: every task has a unique ID and well-defined lifecycle states (
pending,in-progress,completed,failed, etc.), allowing agents to coordinate complex workflows reliably. -
Modality-Agnostic and Multi-Channel: Business communications aren’t just text – they include structured data, files, images, and even audio/video. A2A was built to be multi-modal, allowing agents to exchange rich content in a uniform way. Messages in A2A are composed of Parts, which can be text, JSON data structures, binary files, images, etc., so agents can negotiate the best format for information exchange. For example, an agent could send a chart as raw JSON data or as an image, depending on what the receiving agent can display or process. Agents advertise their supported content types and interaction modes, and the protocol allows dynamic format negotiation on the fly.
Together, these architectural choices make A2A a powerful unifying layer for agent interoperability. It provides a structured yet flexible schema for agent communication (with defined message types, task objects, and error handling), which greatly simplifies development and debugging of multi-agent systems.
Agent Communication Workflow and Components
To illustrate how A2A works in practice, let’s walk through its key components and an example interaction. Two roles are defined in any A2A exchange: a Client Agent (the initiator of a request or task) and a Remote Agent (the recipient that performs the task). These roles are dynamic – the same software could act as a client in one interaction and as a remote (service provider) in another. The protocol defines a small set of standard methods (invoked via JSON-RPC over HTTP) that cover the lifecycle of a task request. The typical sequence is: Discovery → Task Invocation → (Optional Interactive Steps) → Completion.
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Agent Discovery: Before an agent delegates a task, it needs to find a suitable partner agent and understand its capabilities. A2A addresses this with Agent Cards – machine-readable metadata documents (usually served at a standard URL like
https://agent-host/.well-known/agent.json). An Agent Card describes what an agent can do (“skills” or APIs it offers), expected input/output formats, and any authentication requirements or special instructions for connecting. This acts as a kind of “capabilities directory” for the agent. A client agent can fetch the Agent Card of potential collaborators and use it to decide which agent is best suited for a job. -
Task Invocation: Once a target remote agent is selected, the client agent formulates a Task object and sends it via the A2A interface. In A2A, every unit of work is a Task – a structured JSON payload that includes a unique task ID, the action or query, input parameters (as Message parts), and metadata like priority or timeouts. The client uses a JSON-RPC call (e.g.
tasks/send) to submit this task to the remote agent’s A2A endpoint. For example, a Task could be “Generate a quarterly sales report” with an attached JSON dataset. When the remote agent receives the Task, it parses the request and begins processing. -
Streaming and Interaction: Many tasks involve intermediate steps – the remote agent might need more information or wants to provide partial results. A2A supports this through message streaming and iterative calls. The client agent can call
tasks/sendSubscribeto initiate a task and subscribe to updates. The remote agent, if capable, will then send events (over SSE) for progress (TaskStatusUpdateEvent) or partial results (TaskArtifactUpdateEvent). These events keep the client informed in real time, which is crucial in a UX context (e.g. showing a loading bar or intermediate answer to a user) or for orchestrators that need to handle parallel processes. -
Task Completion: Eventually, the task reaches a terminal state – either successfully completed with results, or marked failed (with error info), or canceled. The remote agent’s final response (or a last SSE event) will indicate the completion status and provide any result artifacts. The client agent can then take appropriate action (store the result, present it to a user, trigger the next step in a workflow, etc.).
Security, Trust, and Enterprise Governance
For enterprise adoption, technology must meet stringent security and governance requirements. A2A’s creators understood this, making trust and control a first-class aspect of the protocol rather than an afterthought. Several features underscore A2A’s “enterprise-ready” approach to trust frameworks:
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Authentication and Authorization Flexibility: Unlike closed systems that force a particular auth method, A2A is agnostic and modular in its trust model. An agent can require whatever auth mechanism suits its context – from API keys, OAuth tokens, signatures, to even network-level controls – and it advertises these in its Agent Card. The protocol itself carries auth data as opaque headers or tokens, simply ensuring they are passed along securely.
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End-to-End Encryption and Integrity: All A2A communications occur over secure channels. By using HTTPS as the transport, A2A inherits TLS encryption (data in transit protection) and certificate-based trust between hosts. Agents typically use mutual TLS or token-based auth to authenticate each other at connection time.
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Auditability and Monitoring: Enterprise IT requires visibility into system interactions – who did what, when, and how. A2A’s use of standard web protocols means that all agent interactions can be logged and monitored with familiar tools. Each task request/response can produce an audit trail (with timestamps, agent IDs, task details) which is invaluable for compliance and debugging.
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Data Privacy and Policy Enforcement: In cross-department or cross-company scenarios, it’s vital to control what data is shared. A2A facilitates this by letting agents act as gatekeepers to their own data/tools. An agent will only divulge information explicitly requested and allowed by its logic.
A2A and the Model Context Protocol (MCP)
It is important to clarify how A2A relates to another emerging standard, Anthropic’s Model Context Protocol (MCP). Both A2A and MCP have risen to prominence in the AI ecosystem of 2024-2025, and they address complementary layers of the architecture. In simple terms: A2A governs agent-to-agent interactions (horizontal collaboration), whereas MCP governs agent-to-tool or agent-to-data integrations (vertical connections). MCP is an open protocol that standardizes how AI models or agents can be provided with external context, tools, or data sources. A2A, on the other hand, is about how entire agents (which could themselves be powered by LLMs and MCP under the hood) communicate with each other as peers.
Strategic Implications and Conclusion
The introduction of the A2A protocol signals a significant shift in how enterprises can deploy and scale AI solutions. Strategically, A2A serves as a foundation for the “agentic enterprise”, where autonomous agents become building blocks that can be reconfigured to meet new business needs. By standardizing interoperability, A2A reduces the integration costs and vendor lock-in that have historically hampered enterprise automation. Organizations can mix and match AI capabilities from different providers – for example, using a document analysis agent from Vendor A with a forecasting agent from Vendor B – confident that they will communicate smoothly.
In conclusion, A2A represents the missing “lingua franca” for autonomous agents to talk to each other, unlocking multi-agent orchestration at enterprise scale. By emphasizing interoperability, task-based communication, and modular trust, A2A allows organizations to deploy AI agents not as isolated assistants, but as cooperative members of a broader system.