Most AI agents today operate as islands: an agent built on one platform generally can't hand off a task to an agent built on a different one without custom integration work. A2A protocols define a common language for agents to discover each other's capabilities, pass along task context, and delegate work across those boundaries.

This matters most in a world where a business ends up with multiple AI agents from different vendors handling different functions, a sales agent from one provider, a finance agent from another. A shared protocol means those agents can coordinate a handoff (a sales agent asking a finance agent to check a customer's credit standing) without a bespoke integration for every possible pairing.

Frequently Asked Questions

Why can't AI agents just talk to each other without a shared protocol?

Without a common format for describing capabilities and passing task context, one vendor's agent has no reliable way to understand what another vendor's agent can do or how to hand off a task to it cleanly, the same problem two people would have without a shared language.

How is A2A different from tool calling?

Tool calling connects a model to a defined, static function, look up this record, run this calculation. A2A connects one autonomous agent to another autonomous agent, each capable of its own reasoning, which is a fundamentally more open-ended interaction.

Does A2A replace the Model Context Protocol (MCP)?

No, they address different layers. MCP standardizes how a model connects to tools and data sources. A2A standardizes how independent agents communicate with each other, the two are complementary, not competing standards.

What security concerns come with letting agents from different vendors talk directly?

Authentication (confirming which agent you're actually talking to) and scoping what one agent is allowed to ask another to do both matter a great deal, an open handoff channel between agents is also a potential attack surface if it isn't tightly permissioned.