AI agent architecture is starting to split into two layers.

One layer is about giving an AI assistant access to tools, data, APIs, files, databases, search systems, calendars, ticketing systems, and other external capabilities — and that is where MCP fits.

The other layer is about getting one AI agent to discover, communicate with, delegate to, and collaborate with another AI agent, possibly built by another team, framework, vendor, or organization — and that is where A2A fits.

The annoying part is that both protocols are often discussed as if they solve the same problem, and they do not. There is overlap at the edges, and that overlap is where most of the confusion comes from. But the clean mental model is simple:

MCP is mostly agent-to-tool and A2A is mostly agent-to-agent.