A2A stands for Agent2Agent. In an AI context, it usually refers to a class of protocols or mechanisms that let different agents discover, communicate with, collaborate with, and hand off tasks to one another.
If MCP focuses on "how an agent connects to tools and data," A2A focuses on "how an agent connects to another agent." These two terms often appear together, but they solve problems at different levels.
Grab It in One Sentence First
A2A is a mechanism that lets different AI agents communicate, collaborate, and exchange task status in a standard way.
An everyday analogy is a team. One assistant is responsible for looking things up, one for writing the report, and one for booking the meeting room. If they have no common communication rules, task handoffs get chaotic. This collaboration problem is exactly what A2A aims to solve.
Why Multiple Agents Need a Protocol
As agents gain more capabilities, a system may have more than one agent. A customer-service agent may need to hand a technical problem to an engineering agent; a travel-planning agent may need a price-quoting agent, a scheduling agent, and a payment agent to work together; and within an enterprise, different departments may each have their own AI assistant.
flowchart LR
User["User goal"] --> AgentA["Planning agent"]
AgentA --> AgentB["Retrieval agent"]
AgentA --> AgentC["Execution agent"]
AgentB --> AgentA
AgentC --> AgentA
AgentA --> Result["Aggregated result"]If every agent uses its own way of communicating, collaboration becomes custom integration. A2A's goal is to let agents describe what they can do, receive tasks, return status, exchange results, and complete task handoffs when needed.
The Difference from MCP
MCP can be understood simply as "an agent finding tools," and A2A as "an agent finding another agent." An agent may call files, databases, and APIs through MCP, and may also hand a task to another, more suitable agent through A2A.
The two don't replace each other. MCP solves tool connection, and A2A solves agent collaboration. In a complex system, they may exist at the same time.
What Scenarios A2A Suits
A2A is better suited to scenarios where a task naturally requires division of labor. For example, in enterprise processes, sales, customer service, finance, and the ticketing system each have a different agent; in a multi-expert system, legal, technical, data-analysis, and writing agents each handle a different part; in a long task, one agent handles planning while multiple agents execute subtasks in parallel.
Its value isn't in letting agents chat with each other, but in making capability descriptions, task status, and result exchange more standard.
Where It's Easy to Misunderstand
Multiple agents aren't necessarily better than a single agent. Task splitting, communication cost, context synchronization, and responsibility attribution all get more complex. If a task could originally be completed clearly by one agent, forcibly splitting it into several agents may actually reduce stability.
A2A won't automatically solve security either. When tasks and data are passed between different agents, they still need authentication, permissions, auditing, and user confirmation. Agents being able to communicate doesn't mean every link is reliable.
How to Decide Whether to Use It
When designing an A2A system, make each agent's capability boundaries clear enough: what it can do, what it can't do, what input it needs, what result it outputs, and how it reports back on failure. When agents can take external actions especially, you must set up permissions and confirmation points.