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Agent2Agent Protocol (A2A)

Agent2Agent (A2A) is an open Linux Foundation protocol that lets AI agents from different vendors discover each other, delegate tasks and return results.
Updated October 15, 2026
AI

Definition

The Agent2Agent Protocol (A2A) is an open standard for communication between independent AI agents. It lets an agent discover what another agent can do, hand it a task, follow its progress and receive the results, even when the two agents were built by different companies on different frameworks. Where the Model Context Protocol connects one agent to tools and data, A2A connects agents to each other.

Google introduced A2A in April 2025 and later contributed it to the Linux Foundation, which now hosts the project. Version 1.0, the first stable release, shipped on March 12, 2026, guided by a technical steering committee with representatives from eight technology companies. At its one-year mark in April 2026 the Linux Foundation said more than 150 organizations supported the standard and that it was integrated across Google, Microsoft and AWS platforms. Agents built with frameworks such as LangGraph or CrewAI can use it to delegate subtasks to one another (AI agent frameworks).

The protocol has a few core pieces (A2A specification):

  • The Agent Card, a JSON document usually published at /.well-known/agent-card.json, describes an agent's name, skills, endpoints and authentication requirements. Since v1.0, cards can be cryptographically signed so a client can verify who published them.
  • Tasks are units of work with a defined lifecycle, suited to jobs that take minutes or hours. Agents exchange messages and return outputs as artifacts.
  • Streaming and push notifications report progress on long-running tasks.
  • Transport bindings for JSON-RPC, gRPC and HTTP+JSON, with clients sending an A2A-Version header so agents supporting different versions can negotiate.

Version 1.0 tightened the spec rather than reinventing it. It separated the application protocol from its transport mappings, added multi-tenancy and modernized security flows, and introduced breaking changes in the interaction protocol while letting Agent Cards advertise both v0.3 and v1.0 support during migration.

Other standards build on or align with A2A. Google's Universal Commerce Protocol is designed to be compatible with A2A, as well as with MCP and the Agent Payments Protocol, and the first version of AP2 shipped as an A2A extension. Microsoft's NLWeb project says every NLWeb instance acts as an MCP server and plans A2A support.

For GEO, A2A matters because multi-agent systems change who evaluates your brand. A research or shopping agent may delegate part of a task, such as checking availability or comparing prices, to a specialist agent. The system that decides whether your product or content makes the final answer can be several steps removed from the person who asked, and it may never visit your site directly. This extends the logic of query fan-out: visibility now has to survive delegation between agents as well as parallel searches.

Brands engage with A2A in two ways. Companies that offer bookable or queryable services (travel, retail inventory, B2B software) can publish Agent Cards so other agents can find and call them. Content-focused brands rarely implement the protocol, but they benefit from the same groundwork as agent experience optimization: facts, prices, availability and policies that machines can read and that match across every source.

Examples of Agent2Agent Protocol (A2A)

  • A travel planning agent delegates hotel availability checks to a hotel chain's booking agent over A2A, streams progress to the user and returns two options with live prices.
  • A B2B software company publishes a signed Agent Card at /.well-known/agent-card.json describing its quoting agent's skills and OAuth requirements, so procurement agents can request quotes directly.
  • An enterprise IT team connects a LangGraph-based helpdesk agent and a CrewAI-based provisioning agent through A2A, so password resets that need new hardware are handed off without a human relaying them.
  • A platform team upgrading to A2A v1.0 exposes both a v0.3 and a v1.0 interface in its Agent Card while client agents migrate.

Terms related to Agent2Agent Protocol (A2A)

Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open standard for connecting AI apps to tools and data. Anthropic created it; the Linux Foundation's AAIF now hosts it.

AI

AI Agents

AI agents are AI systems that pursue a goal over many steps: they plan, use tools such as search, a browser or code, act on results and ask humans when needed.

AI

AI Agent Frameworks

AI agent frameworks are SDKs such as LangGraph, CrewAI, the OpenAI Agents SDK and Google ADK that handle tool calls, state and orchestration for AI agents.

AI

Agentic Workflows

Agentic workflows are multi-step processes where an AI model plans, calls tools, checks its own results and loops until a goal is met, with human checkpoints.

AI

Agent Payments Protocol (AP2)

The Agent Payments Protocol (AP2) is an open standard that uses signed mandates to prove what an AI agent may buy and pay for, now run by the FIDO Alliance.

GEO

Agent Experience Optimization (AX)

Agent experience optimization (AX) makes a product, website or API easy for AI agents to discover, understand, access and use on a person's behalf.

GEO

NLWeb

NLWeb is an open protocol and reference implementation, started by Microsoft, that lets a website answer natural language questions from people and AI agents.

GEO

Query Fan-Out

Query fan-out is how AI search splits one question into several sub-queries, runs them at once and merges the results, so pages compete on each sub-query.

AI

Agentic Search

Agentic search is AI search where an agent plans its own queries, reads and compares pages, and acts on the results to finish a task instead of answering once.

GEO

Agentic Commerce

Agentic commerce is buying in which an AI agent searches, compares and completes purchases for a user, using protocols such as ACP, UCP and AP2 to transact.

GEO

Frequently Asked Questions about Agent2Agent Protocol (A2A)

Learn about AI visibility monitoring and how Promptwatch helps your brand succeed in AI search.

MCP standardizes how a single agent connects to tools, data sources and prompts. A2A standardizes how separate agents communicate: discovering each other's skills, delegating tasks, tracking progress and returning results. Many systems use both, with MCP giving each agent its tools and A2A letting agents from different vendors work together on a larger task.

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