Agent2Agent Protocol (A2A)

As organizations deploy AI agents, they quickly find themselves with many of them. A sales platform has its own agent, the HR system has another, the IT service desk has a third and internal teams build more with different frameworks. Each may be capable on its own, but real business processes cross these boundaries. Onboarding a new employee involves HR, IT, facilities and finance. Resolving a customer issue may involve support, billing and engineering. For agents to help with such processes, they need a way to work together even when they come from different vendors. The Agent2Agent Protocol was created to provide that common language.

Google announced A2A in April 2025 with support from a large group of technology partners, and later that year contributed it to the Linux Foundation as an open project. A2A focuses on communication between agents, while the Model Context Protocol focuses on connecting an agent to tools and data. In A2A, each agent publishes a description of what it can do, and other agents can send it tasks, exchange messages and receive results, including for long-running work. The protocol builds on familiar web standards, such as HTTP and JSON, to fit into existing enterprise infrastructure. For AI platform teams, enterprise architects, software vendors and security teams, A2A is part of the emerging infrastructure for multi-agent systems.

Definition of Agent2Agent Protocol (A2A)

The Agent2Agent Protocol is an open communication standard in which AI agents advertise their capabilities through agent cards and exchange tasks, messages and artifacts with other agents over web-based transports, enabling discovery, delegation and collaboration between agents regardless of the vendor, framework or platform used to build them.

It supports multi-agent systems, complements the Model Context Protocol (MCP) and raises questions addressed by agentic AI governance.

Key Concepts in A2A

Agent card. A machine-readable description that an agent publishes, listing its name, skills, endpoint and authentication requirements, so other agents can discover what it does.

Client and remote agents. The client agent delegates work, and the remote agent performs it and reports back.

Tasks. Units of work with a lifecycle, such as submitted, working, input required and completed, which supports long-running activities.

Messages and artifacts. Messages carry instructions and updates between agents, while artifacts are the outputs a task produces, such as documents or data.

Security. The protocol uses standard web authentication and authorization methods, so enterprises can apply existing security controls.

A2A vs. MCP

Purpose. MCP connects an agent to tools, data and resources, while A2A connects agents to other agents.

Interaction style. MCP tools are typically called for specific functions, while A2A supports richer, multi-step collaboration on tasks.

Autonomy. In A2A, the remote agent decides how to accomplish a task, while an MCP tool performs a defined function.

Use together. Many systems use both: agents use MCP to access tools and data, and A2A to delegate work to other agents.

Importance of A2A in Enterprise Operations

It connects agents across vendors. Organizations can combine agents from different platforms into end-to-end processes. Work that crosses departments can be coordinated without building custom integrations between each pair of agents.

It avoids lock-in. An open protocol lets organizations choose the best agent for each job without being tied to one ecosystem.

It also makes delegation visible. Defined tasks and lifecycles make it easier to track what agents are doing on each other's behalf.

A2A and Financial Management

Reusing specialized agents across processes reduces the cost of building separate agents for every workflow. A single well-built agent for a function, such as IT provisioning, can serve many processes.

Agent-to-agent interactions consume model and compute resources, which need to be tracked to understand the cost of automated processes.

Clear task records help attribute costs and outcomes to specific agents, processes and business units.

Security, monitoring and governance for multi-agent systems require investment that should be planned alongside agent deployments.

Common Challenges

The protocol and its tools are still maturing, so features and support vary across platforms. Organizations should expect changes and design integrations that can adapt.

Trust between agents must be managed carefully, since a compromised or poorly designed agent can pass bad instructions to others. Each agent should validate requests and apply its own permissions rather than trusting other agents blindly.

Tracing a process across several agents is harder than tracing a single application. Without shared logging, it can be unclear which agent made a decision or why a task failed.

Organizations may deploy many agents without a clear view of which ones exist, what they can do and who owns them. This makes it hard to manage risk and avoid duplicated effort.