A2A is a protocol for AI agents to discover one another, exchange tasks and collaborate across system and organizational boundaries. Where earlier integration patterns assumed a human or a fixed program on at least one end, A2A assumes neither: two autonomous systems negotiate work between themselves.
That removes the control point most enterprise security was built around. Authorization models assume a principal — a person who is accountable for the action. When an agent delegates to another agent, which delegates again, the chain from the original human authorization to the eventual action gets long, and each hop is a place where scope can widen without anyone deciding that it should.
How it differs from MCP. MCP governs an agent calling a tool: the tool is passive and does what it is told. A2A governs an agent calling another agent, which has its own goals, its own tool access and its own credentials. The trust question is harder because the other side is not a function — it is an actor.
How it differs from a service mesh. A mesh authenticates services and encrypts traffic between them. It does not ask whether the task one agent handed another was within the authority of the human who started the chain. That question is semantic, and it has to be answered where the traffic is, at runtime.
Both agents must be authorized, and the data exchange must comply with policy. APERION's AIDA protocol binds agents to human principals with exact action scopes and limits.
Governing A2A in practice
Governing agent-to-agent traffic comes down to one question asked at every hop: is this task still inside the authority the original human held? Answering it requires three things the protocol does not supply. The agent needs an identity of its own rather than a borrowed credential, so the record names the actor. The authority has to carry a scope that narrows rather than widens as it is delegated. And the decision has to be made where the traffic is, because by the time a log is reviewed the action has happened.
That is the design behind AIDA agent identity and the agent governance approach, worked through in more depth in the AI agent governance guide and the agentic governance documentation. In financial services the same requirement arrives under a different name: FINRA Rule 3110 asks who supervised the conduct, and an agent acting on a borrowed key cannot answer it.
Related terms
Model Context Protocol (MCP) · Policy as code · Prompt injection · Agent authority scope · Agentic AI · Full glossary
Put this in the path of your own agents.
Policy enforced inline between your agents and every model and tool they reach, with a record bound to the human who owns it.
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