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Agent2Agent Protocol (A2A)

Agent2Agent (A2A) is an open protocol for agent-to-agent communication, letting independent AI agents discover each other's capabilities, exchange tasks, and collaborate across vendors.
Updated August 1, 2026
AI

Definition

The Agent2Agent Protocol (A2A) is an open standard, introduced by Google in 2025, that defines how autonomous AI agents talk to one another. Where MCP connects an agent to tools and data, A2A connects agents to other agents: discovering what a counterpart can do, delegating a task, streaming status updates, and returning results across organizational and vendor boundaries.

Capability discovery runs through an agent card, a machine-readable description of what an agent offers, how to reach it, and what credentials it requires. Interactions are modeled as tasks with defined lifecycles rather than one-shot API calls, which suits work that takes minutes or hours and needs progress reporting. Other protocols build on this foundation—AP2, for instance, negotiates payment terms over A2A sessions.

A2A matters to GEO because multi-agent systems change how brands are evaluated. When a research agent delegates a subtask to a specialist agent, your content may be assessed by a system you never receive a request from directly, and the brand that reaches the final answer may have been selected several hops upstream. This extends the reasoning behind query fan-out: visibility has to survive not just parallel sub-queries but delegation between agents.

Practically, brands engage with A2A in two ways. Those building agent-facing products publish agent cards so other agents can discover and use their services. Those focused on content treat it as evidence for agent experience optimization: make capabilities, pricing, and facts machine-readable, because the consumer of that information is increasingly another agent rather than a person.

Examples of Agent2Agent Protocol (A2A)

  • A travel planning agent delegates hotel availability checks to a specialist booking agent over A2A and streams results back to the user.
  • A company publishes an agent card describing its pricing and availability APIs so third-party agents can discover and call them.
  • A procurement agent negotiates terms with a supplier agent using A2A messaging before handing settlement to a payments protocol.
  • A brand notices AI-driven traffic from agents it never sees directly, because a coordinating agent delegated the retrieval step to another system.

Terms related to Agent2Agent Protocol (A2A)

Model Context Protocol (MCP)

Open standard by Anthropic enabling AI models to securely connect with external tools, databases, and services through a universal protocol.

AI

AI Agents

Autonomous AI systems that plan, use tools, execute multi-step tasks, and make decisions to achieve goals with minimal human intervention.

AI

AI Agent Frameworks

AI Agent Frameworks are software libraries and platforms for building autonomous AI agents that can plan, use tools, and complete multi-step tasks, including LangChain, CrewAI, and OpenAI Agents SDK.

AI

Agentic Workflows

AI architectures where models autonomously plan, use tools, browse the web, execute code, and complete multi-step tasks—the evolution from AI chat to AI work.

AI

Agent Payments Protocol (AP2)

AP2 is Google's open protocol for agent-driven payments, using cryptographically signed mandates to define exactly what an AI agent is authorized to purchase on a user's behalf.

GEO

Agentic Search

Agentic search is AI search that plans, browses, compares, uses tools, and completes multi-step research or transaction tasks on behalf of users.

GEO

Agent Experience Optimization (AEO)

Agent Experience Optimization (AEO) is the practice of structuring a website and its data so AI agents can discover, understand, trust, and act on a business—not just read it.

GEO

NLWeb

NLWeb is an open specification that gives websites a natural language interface, letting AI agents query a site conversationally instead of scraping and parsing its HTML.

GEO

Query Fan-Out

Query fan-out is the AI search mechanism where a single query is decomposed into parallel sub-queries, fundamentally changing content visibility.

AI

AGENTS.md

AGENTS.md is a machine-readable instructions file that tells AI agents how to understand, navigate, and act on a website, codebase, or business.

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 accesses tools, data sources, and functions. A2A standardizes how separate agents communicate with each other—discovering capabilities, delegating tasks, and returning results. Most real deployments use both: MCP for the agent's tools, A2A for collaboration with other agents.

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