What Is the A2A Protocol?

2026-04-11 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, long

Summary

The Agent2Agent (A2A) protocol, an open standard initiated by Google Cloud and managed by the Linux Foundation, enables secure, structured communication between AI agents across diverse tools, platforms, and organizations. Unlike the Model Context Protocol (MCP), which focuses on agent-to-tool connectivity, A2A standardizes agent discovery, work exchange, task tracking, and result sharing. Key concepts include the Agent Card, which defines an agent's public profile and capabilities, and the Task, a server-side record of work with a defined lifecycle (submitted, working, completed). Communication occurs via JSON-RPC methods like "message/send" for initiating work, "message/stream" for live updates, "tasks/get" for polling, and push notifications for asynchronous updates. Major cloud providers and enterprise platforms, including Google Cloud, Microsoft Azure, AWS, Salesforce, and SAP, along with frameworks like LangChain and Spring AI, officially support A2A.

Key takeaway

For AI Architects and Software Engineers building multi-agent systems, adopting the A2A protocol is crucial for ensuring interoperability and scalability. You should integrate A2A to enable seamless collaboration between your specialized agents, leveraging its standardized discovery and task management features. This approach will future-proof your AI investments and allow your agents to coordinate effectively across diverse enterprise environments, complementing existing MCP-based tool integrations.

Key insights

A2A standardizes AI agent collaboration, enabling secure, interoperable communication and task management across platforms.

Principles

• Standardize agent discovery and communication.
• Separate agent-to-agent from agent-to-tool connectivity.
• Maintain task state throughout its lifecycle.

Method

Agents define capabilities via an Agent Card. Clients send messages to initiate tasks, which evolve through states. Results are retrieved via blocking responses, streams, polling, or push notifications.

In practice

• Use Agent Card for agent discovery and capability declaration.
• Implement "message/send" for initiating agent tasks.
• Track agent work using the Task object and its lifecycle states.

Topics

• A2A Protocol
• AI Agent Communication
• Model Context Protocol
• Agent Card
• Task Management

Code references

• a2aproject/A2A

Best for: AI Engineer, AI Architect, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.