When people talk about "letting AI act" on company systems, two acronyms keep coming up: A2A and MCP. They are not rival alternatives: they work at different layers of the stack, and often coexist. Understanding which layer you are touching is the difference between a prototype and something you can put into production without scaring the security team.
Two ways to let AI act, two different layers
A2A (Agent-to-Agent) is the more horizontal approach: you make agents talk to each other. MCP (Model Context Protocol) is the more governed approach: you give the agent defined tools and context, with permissions applied to every request.
A2A: agents talking to each other
A2A standardises the dialogue between agents: one agent delegates a task to another and receives the result. It solves orchestration and specialisation, but on its own it does not answer the question "can this agent see this document?". Data access governance sits one layer below, and that is where MCP comes in.
MCP: governed context and tools
MCP exposes well-defined tools and resources to the agent (search, read, update) through a channel where permissions are applied to every request. The source is not copied; it stays in the platform, which filters by role, attribute and row, and logs every access. It is the right layer when real company documents and data are at stake.
Which one should you choose for company documents?
To give AI access to company documents, MCP is the right layer, because it brings context without giving up governance. A2A remains useful for orchestration and specialisation, but on top of sensitive data it must be confined and supervised. In Cervio, the AI channel is an MCP server that inherits the user's permissions, so the agent is a governed client like any other, not an exception to the system.