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Agent-to-Agent Protocol (A2A)

Q: What Is the Agent-to-Agent Protocol?

Definition: The Agent-to-Agent Protocol (A2A) is an open standard that enables AI agents built on different platforms, by different vendors, and running on different infrastructure to discover each other, negotiate capabilities, delegate tasks, and exchange results. If the Model Context Protocol (MCP) is "USB-C for AI tools," A2A is "HTTP for AI agents" — the networking layer that lets agents collaborate across organizational boundaries. A2A was introduced by Google on April 9, 2025 with backing from over 50 technology partners including Atlassian, Box, Salesforce, SAP, and ServiceNow, plus 12 consulting firms such as Accenture, Deloitte, McKinsey, and PwC. Google contributed the protocol to the Linux Foundation in June 2025, establishing vendor-neutral governance. As organizations deploy more AI agents for specialized tasks — one agent for customer support, another for data analysis, a third for content generation — a coordination problem emerges. How does a customer support agent hand off a technical question to a diagnostic agent running on a different platform? How does a research agent discover that a visualization agent exists and can render its findings? Before A2A, the answer was usually custom integration code, shared databases, or manual handoffs by human operators. A2A standardizes this coordination by defining how agents advertise their capabilities, accept tasks from other agents, stream progress updates, and return structured results. The protocol is model-agnostic (it works regardless of whether the agents are powered by GPT, Claude, Gemini, or open-source models), platform-agnostic (agents can run on any cloud or on-premise infrastructure), and opaque by design (agents do not need to share their internal architecture, prompts, or tool configurations — they only expose a well-defined interface).

Q: Who Supports A2A?

A2A has broad industry support across multiple categories: Technology Partners — Over 50 companies backed the initial launch, including Atlassian, Box, Cohere, Elastic, Intuit, MongoDB, PayPal, Salesforce, SAP, ServiceNow, UiPath, and Workday. This breadth of support reflects the universal need for cross-platform agent coordination. Consulting Firms — 12 global consulting firms including Accenture, BCG, Capgemini, Cognizant, Deloitte, HCLTech, Infosys, McKinsey, PwC, TCS, Wipro, and WPP are building A2A into their enterprise AI practices. AI Labs — Anthropic, Google DeepMind, and other major AI labs participate in the protocol's development, ensuring it works well with agents powered by different model families. Linux Foundation — A2A governance was transferred to the Linux Foundation in June 2025, placing it alongside MCP under the same vendor-neutral umbrella organization. This co-governance ensures the two protocols evolve together rather than in competition.

Definition: The Agent-to-Agent Protocol (A2A) is an open standard that enables AI agents built on different platforms, by different vendors, and running on different infrastructure to discover each other, negotiate capabilities, delegate tasks, and exchange results. If the Model Context Protocol (MCP) is "USB-C for AI tools," A2A is "HTTP for AI agents" — the networking layer that lets agents collaborate across organizational boundaries.

A2A was introduced by Google on April 9, 2025 with backing from over 50 technology partners including Atlassian, Box, Salesforce, SAP, and ServiceNow, plus 12 consulting firms such as Accenture, Deloitte, McKinsey, and PwC. Google contributed the protocol to the Linux Foundation in June 2025, establishing vendor-neutral governance.

What Is the Agent-to-Agent Protocol?

As organizations deploy more AI agents for specialized tasks — one agent for customer support, another for data analysis, a third for content generation — a coordination problem emerges. How does a customer support agent hand off a technical question to a diagnostic agent running on a different platform? How does a research agent discover that a visualization agent exists and can render its findings?

Before A2A, the answer was usually custom integration code, shared databases, or manual handoffs by human operators. A2A standardizes this coordination by defining how agents advertise their capabilities, accept tasks from other agents, stream progress updates, and return structured results.

The protocol is model-agnostic (it works regardless of whether the agents are powered by GPT, Claude, Gemini, or open-source models), platform-agnostic (agents can run on any cloud or on-premise infrastructure), and opaque by design (agents do not need to share their internal architecture, prompts, or tool configurations — they only expose a well-defined interface).

How A2A Works

A2A defines four core mechanisms that together enable cross-platform agent collaboration:

Agent Cards

Every A2A-compatible agent publishes an Agent Card — a JSON metadata document hosted at a well-known URL (typically /.well-known/agent.json). The Agent Card describes the agent's name, description, capabilities, supported input and output formats, authentication requirements, and endpoint URL.

Agent Cards serve the same purpose as a business card at a conference: they tell other agents "here is who I am, here is what I can do, and here is how to reach me." Client agents can fetch Agent Cards from a registry or known URLs, evaluate whether an agent's capabilities match a task, and then initiate communication.

Task Lifecycle

When a client agent delegates work to a remote agent, A2A manages the interaction through a structured task lifecycle:

Submitted — The client sends a task request with input data to the remote agent's endpoint.

Working — The remote agent acknowledges the task and begins processing. During this phase, the remote agent can stream intermediate updates back to the client using Server-Sent Events (SSE).

Completed — The remote agent returns the final result, which can include text, structured data, images, audio, or video.

Failed / Canceled — Error handling for tasks that cannot be completed or are explicitly canceled by the client.

This lifecycle gives both agents clear expectations about the state of work without requiring them to share internal implementation details.

Streaming and Push Notifications

A2A supports two communication patterns for long-running tasks:

Streaming (SSE) — For tasks where the client wants real-time progress updates, the remote agent streams incremental results through Server-Sent Events over HTTP. This is particularly useful for generative tasks where partial output is immediately valuable.

Push Notifications — For asynchronous workflows where the client does not want to maintain an open connection, A2A supports webhook-based push notifications. The remote agent sends updates to a callback URL when the task state changes.

Authentication

A2A supports three authentication schemes to accommodate different deployment environments: JSON Web Tokens (JWT) for service-to-service authentication, OpenID Connect (OIDC) for identity federation, and API keys for simpler integrations. The authentication scheme is declared in the Agent Card, so client agents know what credentials to present before initiating a request.

MCP vs A2A: What Is the Difference?

MCP and A2A are the two foundational protocols for the agentic AI ecosystem, and they solve fundamentally different problems:

| Dimension | Model Context Protocol (MCP) | Agent-to-Agent Protocol (A2A) | | --- | --- | --- | | Purpose | Agent-to-tool communication | Agent-to-agent communication | | Analogy | USB-C for AI (connect device to computer) | HTTP for AI (connect computer to computer) | | Created by | Anthropic (November 2024) | Google (April 2025) | | Architecture | Host → Client → Server | Client agent → Remote agent | | Discovery | Capability negotiation per server | Agent Cards (JSON metadata) | | Transport | stdio + HTTP/SSE, JSON-RPC 2.0 | HTTP + SSE, JSON-RPC | | Data types | Text, structured data | Text, images, audio, video streaming | | Governance | Linux Foundation | Linux Foundation | | Key use case | Agent reads a database, calls an API | Agent delegates research to another agent |

The critical distinction: MCP connects an agent to a tool. The tool has no autonomy — it exposes functions and waits to be called. A2A connects an agent to another agent. The remote agent has its own reasoning, can ask clarifying questions, stream partial results, and decide how to accomplish the task.

In practice, an agent might use MCP to access a database and A2A to send the results to a specialized analysis agent on another platform. The two protocols work together as complementary layers of the same stack.

Who Supports A2A?

A2A has broad industry support across multiple categories:

Technology Partners — Over 50 companies backed the initial launch, including Atlassian, Box, Cohere, Elastic, Intuit, MongoDB, PayPal, Salesforce, SAP, ServiceNow, UiPath, and Workday. This breadth of support reflects the universal need for cross-platform agent coordination.

Consulting Firms — 12 global consulting firms including Accenture, BCG, Capgemini, Cognizant, Deloitte, HCLTech, Infosys, McKinsey, PwC, TCS, Wipro, and WPP are building A2A into their enterprise AI practices.

AI Labs — Anthropic, Google DeepMind, and other major AI labs participate in the protocol's development, ensuring it works well with agents powered by different model families.

Linux Foundation — A2A governance was transferred to the Linux Foundation in June 2025, placing it alongside MCP under the same vendor-neutral umbrella organization. This co-governance ensures the two protocols evolve together rather than in competition.

Multi-Agent Collaboration in Practice

While A2A defines how agents collaborate across organizational and platform boundaries, the concept of multi-agent collaboration also applies within a single workspace. Taskade's multi-agent teams demonstrate this at the product level: multiple AI agents with different specializations — research, writing, analysis, design — work together on shared projects within your workspace DNA.

In Taskade, you can assign different agents to different tasks within a project, define orchestration modes that determine how agents hand off work, and let agents trigger automations that chain their outputs together. This is multi-agent collaboration made accessible to non-technical teams — no protocol configuration required.

A2A extends this same paradigm beyond a single platform. In the future, a Taskade agent managing your project board could delegate a competitive analysis task to a specialized research agent running on another service, receive streaming results, and automatically update your project with the findings — all through A2A.

The trajectory is clear: agent collaboration is moving from single-platform teams to cross-platform ecosystems, and A2A provides the infrastructure to make that possible.

Frequently Asked Questions About A2A

What Does A2A Stand For?

A2A stands for Agent-to-Agent Protocol. It is sometimes written as "Agent2Agent" in Google's original documentation. The name directly describes its purpose: enabling communication between AI agents.

How Is A2A Different from MCP?

MCP connects agents to tools — databases, APIs, file systems. A2A connects agents to other agents. An agent might use MCP to read data from a database and A2A to send the results to another agent for analysis. MCP tools are passive (they wait to be called); A2A agents are active (they reason, plan, and stream results).

Is A2A Open Source?

Yes. A2A was contributed to the Linux Foundation in June 2025. The specification and reference implementations are open source under vendor-neutral governance.

Can Different AI Models Communicate Through A2A?

Yes. A2A is model-agnostic. An agent powered by Claude can communicate with an agent powered by GPT or Gemini through A2A, as long as both implement the protocol. The agents do not need to know or care what model powers the other side.

Do I Need A2A to Build Multi-Agent Systems?

Not necessarily. Multi-agent systems can operate within a single platform using internal orchestration — Taskade's multi-agent teams are an example. A2A becomes essential when you need agents on different platforms or from different vendors to collaborate without custom integration code.

What Data Formats Does A2A Support?

A2A supports text, structured data, images, audio, and video streaming. This makes it suitable for a wide range of agent interactions, from simple text-based question-answering to multimodal tasks involving image generation or audio processing.

Model Context Protocol (MCP): The complementary standard for agent-to-tool communication
Multi-Agent Systems: Coordinated AI agent teams that can use A2A for cross-platform collaboration
Large Language Models: The AI models that power agents participating in A2A networks
AI Agents: Autonomous systems that discover and collaborate with other agents via A2A
Autonomous Agents: Independent agents capable of participating in cross-platform A2A networks
Automation: Workflow execution that can be triggered by inter-agent A2A communication