What is Google's A2A and what are the core features of A2A?

Google officially announced the Agent2Agent protocol (A2A) at the Google Cloud Next conference on April 9, 2025, marking a new era of AI agent interoperability. A2A is an open standard designed to enable cross-platform, cross-vendor AI agent collaboration, breaking down barriers between traditional AI systems.

What is the A2A protocol?

A2A (Agent2Agent) is an open protocol from Google designed to enable seamless collaboration between different AI agents. It allows agents from different frameworks, languages, and vendors to share capabilities, assign tasks, and communicate without being restricted to a specific platform. The goal of A2A is to build an "agent web" that enables organizations to build flexible and scalable multi-agent systems.

Core features of A2A

1. Open interoperability

A2A provides a common language that enables AI agents to securely exchange data, negotiate tasks, and share capabilities across platforms and vendors. This eliminates dependence on a single vendor ecosystem and reduces the need for customized integration code.

2. Enterprise-level design

A2A employs authentication, authorization, and encryption mechanisms that are consistent with OpenAPI standards, supports workflows that span hours or days, and provides real-time updates. This makes A2A suitable for enterprise environments that require high levels of security and reliability.

3. Complementarity with MCP

A2A complements Anthropic's Model Context Protocol (MCP), which standardizes agent interactions with tools and APIs, while A2A focuses on inter-agent coordination. Together, they build a comprehensive ecosystem of AI agent interactions.

4. Technical components

• Agent Cards: A JSON metadata file that describes the identity, capabilities, and endpoint information of an agent to facilitate discovery and understanding of its capabilities by other agents.
• task management: Defines the state of a task (e.g., in progress, completed, failed) and supports task assignment and state synchronization between agents.
• Streaming communications and notifications: Real-time updates using HTTP, JSON-RPC and SSE to ensure synchronization of information between agents.

Application Scenarios for A2A

1. Recruitment automation

A hiring manager's agent can collaborate with agents who specialize in candidate screening, resume evaluation, and interview scheduling to create a seamless end-to-end hiring process.

2. Cross-platform workflows

Agents from different systems, such as Atlassian's project management tools and ServiceNow's IT services, can work together to automate complex tasks, such as critical errors reported in ServiceNow automatically creating tasks in Jira and notifying the relevant team members.

Developer Resources and Ecosystems

• open source specification: The full specification of A2A is available on GitHub, with an SDK, sample applications, and support for popular frameworks such as Google's ADK, LangGraph, and CrewAI.
• Agent Development Kit (ADK): Google's ADK allows developers to build multi-agent systems in less than 100 lines of code, with built-in support for A2A and MCP standards.
• Community Support: A growing community of developers and businesses is actively involved in the development of A2A, hosting dedicated forums and regular hackathons to drive innovation.

Comparison of A2A and MCP

concluding remarks

The introduction of the A2A protocol marks a new phase in AI agent collaboration. By providing an open, standardized communication protocol, A2A enables AI agents from different platforms and vendors to seamlessly collaborate and build complex, multi-agent workflows. As more and more enterprises and developers join A2A's ecosystem, the future of AI applications will be more flexible, efficient and intelligent.