AI agents offer a unique opportunity to help people be more productive by autonomously handling many daily recurring or complex tasks. Today, enterprises are increasingly building and deploying autonomous agents to help scale, automate and enhance processes throughout the workplace–from ordering new laptops, to aiding customer service representatives, to assisting in supply chain planning.
To maximize the benefits from agentic AI, it is critical for these agents to be able to collaborate in a dynamic, multi-agent ecosystem across siloed data systems and applications. Enabling agents to interoperate with each other, even if they were built by different vendors or in a different framework, will increase autonomy and multiply productivity gains, while lowering long-term costs.
Today, we're launching a new, open protocol called Agent2Agent (A2A), with support and contributions from more than 50 technology partners.
The A2A protocol will allow AI agents to communicate with each other, securely exchange information, and coordinate actions on top of various enterprise platforms or applications. We believe the A2A framework will add significant value for customers, whose AI agents will now be able to work across their entire enterprise application estates.
A2A focuses on enabling agents to collaborate in their natural, unstructured modalities, even when they don't share memory, tools and context. We are enabling true multi-agent scenarios without limiting an agent to a "tool."
The protocol is built on top of existing, popular standards including HTTP, SSE, JSON-RPC, which means it's easier to integrate with existing IT stacks businesses already use daily.
A2A is designed to support enterprise-grade authentication and authorization, with parity to OpenAPI's authentication schemes at launch.
We designed A2A to be flexible and support scenarios where it excels at completing everything from quick tasks to deep research that may take hours and or even days when humans are in the loop. Throughout this process, A2A can provide real-time feedback, notifications, and state updates to its users.
The agentic world isn't limited to just text, which is why we've designed A2A to support various modalities, including audio and video streaming.
A2A facilitates communication between a "client" agent and a "remote" agent. A client agent is responsible for formulating and communicating tasks, while the remote agent is responsible for acting on those tasks in an attempt to provide the correct information or take the correct action. This interaction involves several key capabilities:
Agents can advertise their capabilities using an "Agent Card" in JSON format, allowing the client agent to identify the best agent that can perform a task and leverage A2A to communicate with the remote agent.
The communication between a client and remote agent is oriented towards task completion, in which agents work to fulfill end-user requests. This "task" object is defined by the protocol and has a lifecycle. It can be completed immediately or, for long-running tasks, each of the agents can communicate to stay in sync with each other on the latest status of completing a task. The output of a task is known as an "artifact."
Agents can send each other messages to communicate context, replies, artifacts, or user instructions.
Each message includes "parts," which is a fully formed piece of content, like a generated image. Each part has a specified content type, allowing client and remote agents to negotiate the correct format needed and explicitly include negotiations of the user's UI capabilities–e.g., iframes, video, web forms, and more.
Video demonstration
Hiring a software engineer can be significantly simplified with A2A collaboration. Within a unified interface like Agentspace, a user (e.g., a hiring manager) can task their agent to find candidates matching a job listing, location, and skill set. The agent then interacts with other specialized agents to source potential candidates. The user receives these suggestions and can then direct their agent to schedule further interviews, streamlining the candidate sourcing process. After the interview process completes, another agent can be engaged to facilitate background checks. This is just one example of how AI agents need to collaborate across systems to source a qualified job candidate.
We're thrilled to have a growing and diverse ecosystem of partners actively contributing to the definition of the A2A protocol and its technical specification. Their insights and expertise are invaluable in shaping the future of AI interoperability.
To learn more about the A2A framework, delve into the full specification draft and explore available code samples to examine the protocol's structure experiment with its code.
