How agents discover and message peers in the emerging A2A framing from the deck (PDF 246–248).
and makes final calls. This is exactly like a manager and their team. One thing we constantly think about when picking a pattern to build a multi-agent system (provided we do need an Agent and it has to be a multi-agent system) is not which one looks coolest, but which one minimizes friction between agents. It’s easy to spin up 10 agents and call it a team. What’s hard is designing the communication flow so that:
● No two agents duplicate work.
● Every agent knows when to act and when to wait.
● The system collectively feels smarter than any individual part.
Agent2Agent(A2A) Protocol Agentic applications require both A2A and MCP.
MCP provides agents with access to tools. While A2A allows agents to connect with other agents and collaborate in teams. Let's clearly understand what A2A is and how it can work with MCP. If you don't know about MCP servers, we covered them in detail in the next section. In a gist: Agent2Agent (A2A) protocol lets AI agents connect to other Agents. Model context protocol lets AI Agents connect to Tools/APIs. So using A2A, while two Agents might be talking to each other...they themselves might be communicating to MCP servers. In that sense, they do not compete with each other. To explain further, Agent2Agent (A2A) enables multiple AI agents to work together on tasks without directly sharing their internal memory, thoughts, or tools.
Instead, they communicate by exchanging context, task updates, instructions, and data. Essentially, AI applications can model A2A agents as MCP resources,
represented by their AgentCard (more about it shortly).
Using this, AI agents connecting to an MCP server can discover new agents to collaborate with and connect via the A2A protocol.
A2A-supporting Remote Agents must publish a "JSON Agent Card" detailing their capabilities and authentication. Clients use this to find and communicate with the best agent for a task. There are several things that make A2A powerful: