The Agent-to-Agent Protocol (A2A) is an open standard developed to enable AI agents from different systems, vendors, or environments to securely communicate, delegate tasks, and exchange results in a consistent, interoperable way. A2A specifies how agents can discover each other’s capabilities, send and receive structured messages, and manage long-running workflows without exposing sensitive internal logic or APIs.

‍

Purpose:

A2A was created to standardize agent-to-agent communication much like MCP standardizes agent-to-tool connections. This improves interoperability, security, and auditability for multi-agent workflows that span teams, organizations, or platforms.

‍

Who maintains it:

Originally announced by Google and now developed in the open with community partners; the project has public specs, SDKs, and samples, and in mid-2025 was donated to the Linux Foundation for neutral governance.

‍

Key Characteristics:

• Standardized Messaging — Defines message formats and transport methods (e.g., JSON-RPC, Server-Sent Events) for structured communication.
• Capability Discovery — Allows agents to advertise their available skills and negotiate interaction parameters.
• Workflow Coordination — Supports asynchronous tasks, streaming responses, and status updates.
• Security-Aware Design — Incorporates authentication, encryption, and access control to protect communications.

Use Cases:

• Coordinating multiple specialized AI agents on a shared project.
• Allowing enterprise agents to interact with trusted partner agents across organizations.
• Delegating sub-tasks to domain-specific agents without direct API integration.

Security Considerations:

A2A deployments should implement mutual authentication, encrypted transport, and comprehensive logging to ensure agent interactions are both trusted and auditable.

‍

Example in Practice:

An enterprise orchestration agent could use A2A to delegate data-cleaning to one agent and report-generation to another, each hosted in different departments, and combine the outputs into a final deliverable — all while respecting each team’s security boundaries.

‍

Related terms:

• AI Agent Protocol – A standardized method for AI agents to securely communicate, negotiate, and exchange data with other agents, tools, or services.
• Model Context Protocol (MCP) – A protocol for connecting AI agents to external tools, data sources, and applications.
• MCP Client – A component that connects to MCP servers to request and use tools or data.