Agent-2-Agent Protocol (A2A) - A Deep Dive

Ever wondered what happens when AI agents finally start talking to each other as effortlessly as we do? In this post, we go behind the scenes of Google's brand-new Agent2Agent (A2A) protocol—this is exactly what A2A promises to deliver, enabling autonomous systems to coordinate, delegate, and evolve together. You'll see how it works, why it could reshape the AI landscape, and what every business leader needs to know to stay ahead of the curve. 

Buckle up: by the end, you'll see why A2A might just be the next big leap in intelligent automation

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Introduction

Agent2Agent (A2A) is an open protocol that provides a standard way for AI agents to collaborate with each other, regardless of the underlying framework or vendor. A2A's goal is to scale enterprise-level agentic systems. When we say A2A is scalable, we mean it can coordinate hundreds—or even thousands—of autonomous agents across distributed environments without bottlenecks: new agents simply announce themselves via standardized documentation called Agent Cards, and existing agents can discover and collaborate with them immediately. 

By enhancing collaboration, A2A lets each agent share, not just final outputs, but real-time task updates and intermediate artifacts—whether that's streaming partial results from a complex data analysis or handing off subtasks to domain-specific agents—so workflows can be dynamically composed and optimized on the fly. Its security model isn't an afterthought, either: every JSON-RPC call runs over HTTPS, supports mutual TLS or OAuth for authentication, and includes built-in observability hooks so organizations can audit which agent performed which action and when. Finally, A2A's flexibility shines in its model-agnostic design: whether your agents run proprietary LLMs behind the corporate firewall or lightweight rule-based engines in the cloud, the same protocol carries tasks of any duration, size, or complexity—no custom adapters required.

A2A promises to unlock transformative business outcomes—more on those in a moment—but at its core, it lets agents weave seamlessly across your existing applications, collaborating in real time to slash cycle times and boost operational efficiency. Developers gain the freedom to build new capabilities that instantly interoperate with any other A2A-compliant agent, and end users can mix and match best-of-breed services from different providers without custom integration work. In short, A2A is becoming the de facto standard for AI agents on the web: enabling discovery, communication, and coordinated action with one driving vision: "Make AI agents universally interoperable!" (Google) 

What is A2A?

Here is a schematic showing the basic architecture of A2A:

A2A by default manages communication between a "client" agent and one or more "remote" agents, also called "A2A servers". The client agent is the "user-facing" agent whose role is to receive tasks from the user, analyze them, and then delegate them to the remote agent best suited for that task. The end-to-end flow of this communication involves various key steps including agent discovery, authorization, communication/streaming of tasks and responses, and task management. This communication between the A2A server and the client agent happens in a secure manner through A2A.

A2A consists of three components: a user, a client agent or A2A client, and a remote agent or A2A server. 

User

The user is the end user of the agentic system, who requests a task from the "client" agent. It can be a human or an ai agent as well. Imagine yourself planning to book a flight through a company whose entire flight booking management system is run by autonomous AI agents communicating tasks & responses through the A2A protocol. In this example, you are the user of this system. 

Client

A client agent serves as the user-facing orchestrator: when a user submits a request, the client wraps it into a task, assigns a unique identifier, and tracks its state (e.g. working, completed, failed). Because tasks often involve multiple back-and-forth exchanges, A2A maintains state so that each stage of the interaction—sending inputs, receiving partial or final outputs—can be correlated to the correct task. To determine which remote agent can handle the work, the client fetches each agent's Agent Card metadata (a JSON file typically served from https://{agent_domain}/.well-known/agent.json). These Agent Cards describe an agent's identity (name, version, description), declared capabilities such as Streaming or PushNotifications, supported authentication methods (OAuth, mTLS), and default input/output modes. Below is how the agent card would look like for the Booking Agent of the flight booking company.

Armed with these details, the client selects the best-fit agent—sometimes by running an LLM over the collected Agent Cards—and delegates the task over the A2A protocol. Once delegation occurs, the client begins exchanging Parts (the smallest content units—TextPart, DataPart, or FilePart) according to the chosen communication style. A client agent can be as sophisticated as an AI agent or as simple as an API endpoint on a user interface.

Remote Agent

A remote agent implements the A2A standard as a "black-box"; clients never need to know its internal workings—only its contract. This implies that for our flight booking system the web interface (essentially serving here as the client), does not need to know about how the booking or the payment agents actually work, all it has, is the access to the API endpoints of each of these agents over which the entire communication happens. The heart of its discoverability is the Agent Card JSON. If this metadata contains sensitive details, the agent must protect the endpoint (e.g. via mTLS or authenticated access). Beyond discovery, the agent exposes an HTTP JSON-RPC interface: when the client invokes methods like "message/send" or "message/stream", the agent processes the task internally and emits one or more Artifacts—bundles of Parts that can range from simple text responses to full-fledged documents or streamed updates.

Client-Server Interactions

Clients and remote agents choose among three interaction styles depending on task requirements. In a simple request/response exchange, the client calls "message/send" and the agent returns a completion status plus any resulting Artifact. For long-running jobs that benefit from incremental feedback, the client opens a Server-Sent Events stream via "message/stream", allowing the agent to push real-time event updates (such as partial results or progress messages) until the task concludes. Alternatively, clients can register a webhook through "tasks/pushNotificationConfig/set", and the agent will POST status updates to that URL whenever the task's state changes. A flowchart here would branch these three paths—single-call RPC, continuous SSE stream, or webhook callbacks—and show how each carries Parts and Artifacts back to the client. 

An interesting question that might arise at this point is what happens if there are more than one available remote agents which can fulfill a task? 

One of the solutions to this that is being used is by making an LLM do the delegation process on behalf of the client agent. The LLM decides and chooses which agent to delegate the task to, based on the available agent cards and from there the normal flow resumes.

Security considerations for A2A

A2A treats each agent as a first-class, HTTP-based enterprise application, leveraging existing infrastructure rather than inventing new protocols. At its core, A2A requires that every interaction happens over HTTPS with modern TLS versions (1.2 or higher) and strong cipher suites, ensuring that data in transit cannot be snooped or tampered with. Agents advertise their authentication requirements in their Agent Cards—whether OAuth2, mTLS, API keys, or OpenID Connect—and clients obtain and present those credentials out of band, never embedding secrets inside the JSON-RPC payload itself. Once authenticated, agents enforce fine-grained authorization policies, granting only the minimum privileges needed to invoke particular "skills" or access specific data domains.

Beyond simple request/response, A2A supports streaming of intermediate results and task-lifecycle events, all while remaining agnostic to the models or frameworks powering each agent. Data privacy is front and center: implementers must consciously minimize sensitive fields in messages and artifacts, comply with regulations like GDPR or HIPAA, and secure any persisted data using enterprise-grade encryption. Finally, because A2A rides on HTTP, it integrates naturally with distributed tracing (via W3C trace context headers), comprehensive logging (correlating task IDs, trace IDs, and session IDs), and metrics platforms—giving operators end-to-end visibility into agent workflows.

Despite these safeguards, several risks remain. Misconfigured TLS (weak ciphers, expired certificates) or skipped certificate validation can expose communications to "man-in-the-middle" attacks. Poorly managed credentials—long-lived tokens or improperly scoped API keys—may lead to unauthorized access or replay attacks. Overly permissive authorization policies could allow agents to perform unintended actions, resulting in data leaks or corruption. Inadequate data-handling practices may inadvertently log or store sensitive information, while gaps in tracing and monitoring can leave critical failures undetected until they escalate.

To mitigate these threats, organizations should enforce strict TLS configurations and automate certificate renewal, use short-lived, scope-limited tokens stored in secure vaults, and rotate credentials regularly. Authorization policies must be reviewed periodically, with automated tests ensuring each Agent Card exposes only intended capabilities. Implement data-minimization practices, redact before logging, and apply encryption both in transit and at rest with aggressive retention and purge policies. Lastly, instrument all agents comprehensively—using sampling where necessary to manage volume—and routinely audit logs and traces to confirm end-to-end coverage and rapid incident response.

A2A, MCP and Other Services

While A2A is a promising technology enabling seamless agent interoperability, it is important to clarify the specific problems it does—and does not—tackle directly. The following clarifications highlight common misconceptions, ensuring organizations set accurate expectations when integrating A2A into their workflows.

Firstly, A2A is not designed as a client–server tool invocation protocol like MCP. Unlike MCP, which primarily focuses on invoking remote tools in a request–response fashion, A2A prioritizes decentralized, peer-to-peer agent collaboration, maintaining its own task lifecycle management. Secondly, A2A does not act as a centralized orchestration service. It does not mandate or provide a central broker or hosted orchestration hub; rather, it defines a purely decentralized protocol specification, allowing any compliant agent implementation to seamlessly collaborate. Additionally, while A2A incorporates certain enterprise-level features such as authentication and observability, it is not intended as a comprehensive security or data-governance framework. Organizations using A2A will still need to layer in encryption, access control, and policy enforcement to fully meet their specific compliance requirements. Finally, A2A does not inherently handle state persistence or memory-sharing. Agents can exchange contextual and data information via Parts, but the protocol itself does not provide built-in mechanisms for long-term storage, state persistence, or memory snapshot and restoration.

However, while A2A itself does not address these areas, it is explicitly designed to integrate seamlessly with external solutions that do. Tools such as centralized orchestration services, data governance frameworks, or memory management systems can—and ideally should—be combined with A2A to provide a more robust overall solution. For instance, MCP, a tool invocation protocol, can be used alongside A2A by allowing A2A agents to invoke MCP-managed tools. This combination leverages A2A's agent-to-agent collaboration with MCP's tool invocation strengths, creating a powerful synergy.

A2A and MCP differences and Integration

Here I will be focusing more from the business & architectural perspective, for a more technical deep dive, you can always refer to this article here where the author goes through the technical details of MCP.

MCP is about giving your agents access to the outside world while A2A is about allowing your agents to discover, communicate and collaborate with each other.  While A2A tackles the orchestration of multi-agent workflows, MCP is purpose-built for seamless integration of a single agent with external services. A2A is stateful while MCP is stateless. A2A's design emphasizes asynchronous streaming, dynamic discovery, and capability-based task delegation. In contrast, MCP focuses on synchronous request–response exchanges to inject context into LLM-driven applications.

But they are not as different and isolated as it might look like from the above points. In fact, their difference is what makes them complement each other even better, from both an architectural sense and a technical sense. An agentic application might use A2A to communicate with other agents, while each agent internally uses MCP to interact with its specific tools and resources. They are not rivals; they are friends! 

Technically, MCP and A2A form a natural partnership: A2A provides the horizontal integration layer for peer-to-peer, multi-agent workflows, while MCP delivers vertical integration by connecting agents to specialized external services and tools. In practice, an agent might use A2A's capability-based Agent Cards to discover which peers can collaborate on a complex task, leverage A2A's asynchronous streaming and lifecycle events to coordinate work across those peers, and then invoke MCP's synchronous, schema-enforced JSON-RPC methods to call out to a trusted external tool for a specific operation. This blend of decentralized task orchestration with reliable, tool-level invocation gives agents both the flexibility of peer collaboration and the precision of strict API contracts.

From a business perspective, pairing A2A with MCP lets organizations build modular, scalable systems that are both flexible and reliable. By treating A2A as the "mesh" layer for agent-to-agent collaboration, teams can rapidly onboard new capabilities—whether developed in-house or partner-provided—without altering core services. MCP then serves as the vertical "plumbing," giving each agent a standardized, schema-driven way to call out to specialized tools, third-party APIs, or legacy systems. This separation of concerns accelerates development (since the agent network and tool integrations evolve independently), reduces vendor lock-in (you can swap out a tool behind an MCP interface without disrupting your agent mesh), and simplifies compliance and monitoring (A2A handles secure peer interactions while MCP enforces strict input/output validation). In turn, businesses can launch new offerings more quickly, test novel integrations with minimal risk, and adapt their ecosystem dynamically as requirements shift—delivering a competitive edge in a fast-moving market. Zooming out, from an architecture and business standpoint, A2A and MCP also work well together. 

One thing that I have learnt the most in my 1 month as an MLOPs Intern here at Worldwide is that a technology becomes useful only if it adds value. The one phrase that I hear the most here is "What value does it bring to the organization/enterprise using it?" And so, I want to use this section to ask this question about how MCP and A2A can build upon enterprise architectures. For our purposes, let us use the example of a company that does flight bookings but has now shifted towards a service completely staffed by autonomous AI agents, each specialized in a different aspect of the trip. Agents coordinate via A2A for high-level task hand-offs and invoke tools via MCP for structured operations.

A2A and MCP Integration Example: The Flight Booking System

Consider a flight bookings company that seeks to fully automate its reservations workflow and elevate the customer experience beyond basic FAQ chatbots. Previously, a standalone support agent could handle simple queries—flight times, baggage policies, or check-in procedures—but it lacked the ability to complete end-to-end tasks such as selecting flights, securing seats, or updating a user's calendar.

To address these limitations, the company adopted a hybrid A2A + MCP architecture. In this design, specialized agents collaborate peer-to-peer via A2A: for example, a Booking Agent discovers available flights and confirms reservations, a Calendar Agent schedules itineraries, and a Payment Agent manages the payments of the users. Each of these agents accesses its respective external service—flight search APIs, booking engines, and calendar platforms—through MCP's schema-validated JSON-RPC interfaces.

1. Customer Inquiry (User → Booking Agent via A2A)

1. Customer sends an A2A message to the Booking Agent:
   "I need to fly from New York to Paris on July 10 and return on July 20. Please find me the best options."
2. The Booking Agent engages in a multi-turn conversation over A2A to clarify preferences:

• "Do you have a preferred airline or cabin class?"
• "Would you like direct flights only, or are connections acceptable?"

2. Flight Search (Booking Agent → Web Search Tool via MCP)

Once details are confirmed, the Booking Agent uses MCP to invoke the specialized flight-search tool:

• Flight Search Tool responds with a list of available itineraries and prices.
• The Booking Agent selects the optimal option and confirms back to the customer over A2A.

3. Calendar Scheduling (Booking Agent → Calendar Agent via A2A)

After flights are booked, the Booking Agent delegates trip scheduling to the Calendar Agent:

• Booking Agent → Calendar Agent (A2A)

"Please add the confirmed flight dates and times to my calendar, including   reminders 24 hours before departure."

4. Calendar Entry (Calendar Agent → Calendar Tool via MCP)

The Calendar Agent invokes its calendar-management tool via MCP:

• A second MCP call schedules the return flight event on July 20.
• The Calendar Tool confirms creation of both events and reminders.

5. Payment Processing (Booking Agent → Payment Agent via A2A)

Finally, the Booking Agent asks the Payment Agent to complete the transaction:

•  Booking Agent → Payment Agent (A2A)
   "Charge my corporate card ending in 1234 for the total fare of $1,250.00."

6. Payment Execution (Payment Agent → Account Tool via MCP)

The Payment Agent calls its account-management tool to request payment:

• The Account Tool returns a payment confirmation or error code.
• The Payment Agent reports success back to the Booking Agent via A2A, which notifies the customer that the trip is fully booked and paid.

Summary:

• A2A handles conversational, multi-agent choreography: clarifying requirements, delegating tasks, and notifying stakeholders.
• MCP powers precise tool interactions: searching flights, creating calendar entries, and executing payments through well-defined JSON-RPC calls.

Together, A2A and MCP enable a seamless, modular travel-planning ecosystem in which specialized agents can both talk to each other and reliably invoke the services they need. ­This synergy of A2A's decentralized task orchestration with MCP's reliable tool invocations allows the company to deliver a seamless, end-to-end booking experience. New agents and external tools can be introduced independently—whether adding a mobile-wallet payment service or a loyalty-points engine—without disrupting the core workflow, thus enabling rapid innovation and robust scalability. 

Business Implications of A2A

With A2A in play, enterprises can look forward to transformative business outcomes rather than just another technical protocol. First, by enabling agents to collaborate seamlessly across silos—whether that's finance, sales, customer support, or supply chain—you unlock end-to-end automation that drives down operational costs and accelerates cycle times. Imagine a single customer inquiry that once touched three teams now being resolved in seconds by a choreographed agent workflow, freeing up human talent for higher-value work.

Second, A2A fosters rapid innovation and partner ecosystems. When your developers can mix and match third-party agents alongside in-house services simply by sharing Agent Cards, you shorten integration lead times from months to days. That agility translates into faster time-to-market for new offerings—everything from personalized financial advice bots to on-demand maintenance schedulers—or even entirely new revenue models based on "agent-as-a-service."

Third, small and medium-sized teams can build their own specialist agents—say, an SME team focused on regulatory compliance or field-service diagnostics—without ever worrying about custom integration work. Because any A2A-compliant agent automatically discovers and interoperates with the rest of the mesh, those teams can innovate rapidly in their niche while still collaborating effortlessly with corporate systems and partner services.

Fourth, the protocol's built-in observability and security features give executives confidence to scale agent deployments across mission-critical processes without sacrificing compliance or auditability. You get clear traceability of which agent did what, when, and under what policy, helping you meet regulatory requirements and internal governance standards at enterprise scale.

Finally, universal agent interoperability becomes a competitive moat: by lowering the friction for co-innovation with partners, you extend your digital footprint into adjacent markets and create sticky ecosystems. In other words, A2A doesn't just make your AI agents talk to each other—it helps your organization move faster, innovate boldly, and compete more effectively in an AI-driven economy.

Conclusion

In summary, by combining A2A's peer-to-peer orchestration with MCP's tool interface, organizations can unlock a new era of truly modular, end-to-end agent ecosystems—whether automating complex customer journeys, streamlining back-office workflows, or powering next-generation research assistants.  With A2A they can ensure secure discovery and task delegation across any compliant agent. As more vendors and open-source communities build on these complementary standards, we can expect an explosion of interoperable AI applications—each agent playing to its strengths, yet seamlessly collaborating to deliver richer, more scalable solutions.  If you're building or evaluating agent-powered systems today, adopting A2A and MCP together is the clearest path to future-proof interoperability and rapid innovation.

Author's Note
My heartfelt thanks go to Sally Jankovic & Adrien Hernandez for their invaluable insights and support in crafting this blog. I'm also deeply thankful to Chris Carpenter and Yoni Malchi for providing their highly specific, business-focused feedback, which significantly strengthened and refined this blog!