Today's businesses have started to view artificial intelligence (AI)-based solutions not only as tools for automation but also as essential building blocks that transform decision-making processes. In particular, the rise of autonomous software agents in the business world enables the more efficient and error-free execution of complex processes. However, fully realizing this potential depends on the ability of these agents to communicate effectively with each other, i.e., their interoperability.

In this context, the Agent-to-Agent (A2A) protocol, introduced by Google, is an open and scalable communication framework designed to overcome the key technical barriers faced by multi-agent systems. A2A allows AI agents developed by different manufacturers to communicate securely and efficiently with one another. Over 50 technology and service providers support the protocol. This broad ecosystem accelerates the protocol's adoption in enterprise applications.

A2A Protocol Design Principles

A2A has been developed based on the following five core principles:

1. Support for Agent Capabilities: Communication between agents enables natural and flexible collaboration without the need for pre-shared context, memory, or tools.
2. Built on Existing Standards: The protocol is built on common internet protocols such as HTTP, SSE, and JSON-RPC, making it easy to integrate with existing IT infrastructures.
3. Default Security: Secure data exchange is ensured through enterprise-grade authentication and authorization mechanisms.
4. Long-Term Task Support: Agents can share information concurrently not only for short-term tasks but also for long-term research tasks that may span hours or days, involving human interaction.
5. Modality Independence: It supports the processing of various data types such as text, voice, and video, enabling multi-modal communication.

How the A2A Protocol Works

A2A is based on a task-driven communication model between a client agent and a remote agent. In this model, the client agent defines the task, and the remote agent attempts to carry it out. The process includes the following steps:

• Capability Discovery: Each agent defines its tasks and capabilities in a JSON format called an "Agent Card." This card helps the client agent select the most appropriate remote agent.
• Task Management: Agents communicate through task objects during the initiation, monitoring, and completion of specific tasks. Task outputs are referred to as "artifacts."
• Collaboration and Content Negotiation: Agents can share the task context, user instructions, or created content (e.g., images, videos). The content of messages can be formatted according to the target agent's user interface capabilities.

Real-World Application: Recruiting Software Engineers

A concrete example of A2A’s potential is in the recruitment process for software engineers. For instance, an HR manager defines candidate criteria and assigns a task to their agent. This agent, through the A2A protocol, connects with other agents to research suitable candidates. Once candidates are listed, the agent organizes interview schedules, and another agent conducts security checks. The entire process is managed through a single interface, without manual system transitions or context loss.

Looking Ahead

The A2A protocol represents a significant step toward enabling AI agents not just as individual tools but as collaborative digital coworkers. For organizations, this means lower operational costs, higher efficiency, and faster decision cycles. Developed as an open-source solution by Google and its partners, this protocol is not only a technical solution but also a paradigm shift in digital transformation.