Agent-to-Agent (A2A) communication is a new frontier in AI development, allowing independent, specialized AI agents to collaborate on complex tasks. By establishing a common protocol, developers can build sophisticated, multi-agent systems that are not locked into a single vendor or platform. This guide explains the open A2A Protocol, how it works with related standards like the Model Context Protocol (MCP), and how to implement it for secure, scalable agent collaboration.

The Agent-to-Agent (A2A) Protocol is an open standard designed to facilitate communication and collaboration between distinct AI agents. It defines a common language and set of rules that agents can use to discover each other, negotiate tasks, and exchange information securely. Think of it as a diplomatic protocol for software agents, enabling them to work together regardless of who built them or where they are hosted.

This interoperability is crucial for moving beyond monolithic AI systems and toward a more dynamic, decentralized ecosystem of specialized agents.

The A2A Protocol is built on modern, web-friendly standards to ensure broad compatibility and ease of implementation. At its core, it leverages JSON-RPC 2.0, a lightweight remote procedure call protocol that uses JSON for its data format.

Here are the key components of the A2A protocol:

• Agent Cards: Each agent has a discoverable “Agent Card,” which is a standardized JSON document. This card acts like a business card, providing essential metadata such as the agent’s name, its capabilities, the tasks it can perform, and the endpoint (URL) for communication.
• Discovery: Agents find each other by retrieving these Agent Cards. This can be done through a central registry, a direct lookup, or other discovery mechanisms. Once an agent has another’s card, it knows how to initiate a conversation.
• Task Negotiation: Communication begins with a handshake and task negotiation. The initiating agent proposes a task, and the receiving agent can accept, reject, or propose modifications. This negotiation ensures both agents agree on the scope and parameters of the work to be done.
• Communication Semantics: A2A supports various communication patterns, including simple request/response, streaming data for long-running tasks, and push notifications to deliver unsolicited updates. This flexibility allows it to support a wide range of collaborative workflows.
• Security: Security is a fundamental aspect of the protocol. It uses standard authentication and authorization mechanisms to ensure that agents are who they say they are and have the necessary permissions to request or perform tasks. Security scopes are defined within the Agent Card, specifying what an agent is allowed to do.

The A2A protocol is often used in conjunction with the Model Context Protocol (MCP), a complementary standard that allows agents to access external tools and data sources in a structured way.

• MCP focuses on connecting a single agent to various tools (e.g., APIs, databases, functions). It provides context to the agent about what tools are available and how to use them.
• A2A focuses on connecting multiple agents to each other.

When used together, MCP can provide the tools an agent needs to perform a task, while A2A allows that agent to collaborate with other agents, delegate sub-tasks, and synthesize results. For example, an agent connected to a flight search API via MCP could collaborate via A2A with another agent specialized in hotel bookings to plan a complete trip.

The ability for agents to collaborate unlocks powerful new applications that would be difficult to implement with a single, monolithic AI model.

• Cross-Platform Integration: An agent within Microsoft Copilot Studio could delegate a specialized data analysis task to a custom agent running on a different cloud platform.
• Enterprise Workflows: A customer support agent could escalate a complex issue by passing the case to a technical support agent, which then collaborates with a billing agent to process a refund—all happening seamlessly in the background.
• Supply Chain Automation: An agent monitoring inventory levels could trigger a purchasing agent to order new stock, which in turn communicates with a logistics agent to arrange for delivery.

These examples highlight how A2A enables the creation of a “society of agents,” where each member has a specific skill and can contribute to a larger goal.

While powerful, building and managing multi-agent systems with A2A comes with its own set of challenges:

• Service Discovery: How do agents find each other in a dynamic environment where new agents may come online or existing ones may be updated?
• Security and Trust: How do you manage identities and permissions between agents built by different teams or organizations? Establishing a robust trust framework is critical.
• Task Decomposition: Breaking down a complex problem into sub-tasks that can be effectively delegated requires careful design of both the agents and the tasks themselves.
• Error Handling and Resilience: When one agent in a chain fails, the entire workflow can be disrupted. Designing for failure and implementing retries or alternative paths is essential.

While the A2A protocol defines how agents talk to each other, the overall system still requires robust security and user management, especially when humans are in the loop. This is where Kinde provides critical infrastructure.

An A2A-powered application isn’t just agents; it’s a product that users interact with and administrators manage. Kinde helps secure this ecosystem by managing identities for all parties—not just the agents.

• Securing Human-to-Agent Interaction: When a user kicks off a task for an AI agent, Kinde can authenticate the user and pass their identity and permissions to the initial agent. This ensures the agent is acting on behalf of an authorized user.
• API Authorization for Agents: You can secure the administrative endpoints of your agent platform using Kinde’s API authorization. This ensures that only trusted services or developers can configure, deploy, or manage agents. Kinde’s support for OAuth 2.0 and the client credentials grant is ideal for these machine-to-machine security scenarios.
• Custom Claims for Granular Control: Kinde allows you to embed custom claims into access tokens. These claims can represent user roles, subscription levels, or specific permissions that the agent system can use to make fine-grained authorization decisions, ensuring agents only perform actions the user is entitled to.

By handling the foundational identity and access management, Kinde lets you focus on building intelligent agent workflows, confident that the interactions are secure and authorized.

For more on securing applications and APIs, explore the following Kinde documentation:

• Register and manage your APIs
• M2M overview