---
title: "A2A Protocol"
description: "An open standard introduced by Google in April 2025 that enables AI agents to discover, communicate with, and delegate tasks to each other autonomously across different platforms and vendors."
url: "https://skribra.com/glossary/a2a-protocol"
type: "glossary-term"
category: "AI & Automation"
updated: "2026-06-08"
---

# A2A Protocol

**Definition.** An open standard introduced by Google in April 2025 that enables AI agents to discover, communicate with, and delegate tasks to each other autonomously across different platforms and vendors.

**In short.** A2A (Agent-to-Agent) is a standard protocol that lets AI agents talk to each other. Where one AI might control tools (via MCP), A2A lets multiple specialized AI agents collaborate on complex, multi-step tasks — like an automated content pipeline.

## Key points

- A2A enables 'horizontal integration' — peer-to-peer collaboration between AI agents — complementing MCP's 'vertical integration' of agents to tools and data sources
- Key components: Agent Cards (capability profiles), Tasks (discrete work units), Messages (JSON-RPC over HTTPS), and Artifacts (shared data/outputs)
- Backed by Google, the Linux Foundation, and major enterprise partners, A2A is becoming foundational infrastructure for multi-agent AI workflows
- For content and SEO, A2A enables autonomous pipelines where research agents, writing agents, optimization agents, and publishing agents collaborate without human coordination

## How A2A Protocol Works

A2A operates on a client-server model where AI agents advertise their capabilities through Agent Cards — structured profiles describing what tasks the agent can perform, what inputs it accepts, and what outputs it produces[1]. A client agent (orchestrator) reads Agent Cards to discover suitable agents for a given task, then delegates work via Task objects with clear lifecycle states (submitted, in-progress, completed, failed). Communication happens through standardized JSON-RPC messages over HTTPS[2]. Artifacts — the outputs of completed tasks (documents, images, data files) — are shared between agents in a structured format. This architecture allows any compliant agent to interoperate with any other, regardless of the underlying AI model or vendor — enabling cross-company [AI content pipelines](https://skribra.com/glossary/ai-content-generation) that were previously impossible.

## A2A vs. MCP: Complementary Protocols

MCP (Model Context Protocol) and A2A address different integration layers[1]. MCP focuses on connecting a single AI agent to external tools and data sources — giving one agent the ability to search the web, query databases, or call APIs. A2A focuses on connecting multiple AI agents to each other — enabling an orchestrating agent to delegate specialized subtasks to other agents. In a content production pipeline, MCP might connect a research agent to web search and [keyword databases](https://skribra.com/glossary/keyword-clustering), while A2A enables that research agent to hand its findings to a writing agent, which hands its draft to an SEO optimization agent, which delivers the final piece to a publishing agent. These protocols are designed to work together, not compete[2].

## A2A in SEO and Content Workflows

For SEO and content teams, A2A represents a shift toward fully autonomous content operations[3]. A multi-agent pipeline could include: a strategy agent that identifies [keyword](https://skribra.com/glossary/keyword-difficulty) opportunities from [SERP](https://skribra.com/glossary/serp) data, a research agent that gathers context and competitor analysis, a writing agent that produces a full draft, an optimization agent that checks [semantic keyword](https://skribra.com/glossary/lsi-keywords) placement, internal links, and readability, and a publishing agent that formats and submits to the CMS — all without human coordination. Today, platforms like Skribra [implement similar pipelines](https://skribra.com/how-it-works) as single-system architectures. As A2A matures, these pipelines will become composable from best-in-class specialist agents from different vendors, driving [Organic Traffic](https://skribra.com/glossary/organic-traffic) growth at a scale not previously possible with human-led teams.

## Related terms

- [AI Content Generation](https://skribra.com/glossary/ai-content-generation): The use of large language models (LLMs) and related AI technologies to automatically produce written content such as blog articles, product descriptions, social media posts, and SEO copy.
- [Organic Traffic](https://skribra.com/glossary/organic-traffic): Website visitors who arrive through unpaid search engine results, as opposed to paid ads, social media, direct visits, or referral links.
- [SERP](https://skribra.com/glossary/serp): The page Google or another search engine displays in response to a user's query, containing organic results, paid ads, and rich features like featured snippets, image packs, and knowledge panels.

## Sources

1. [Google Developers Blog — A2A: A New Era of Agent Interoperability](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/)
2. [A2A Protocol — Official Specification](https://a2a-protocol.org/latest/specification/)
3. [A2A Protocol — GitHub Repository](https://github.com/a2aproject/A2A)

## Machine-readable index

- HTML version of this page: https://skribra.com/glossary/a2a-protocol
- Site index for agents: https://skribra.com/llms.txt
- Sitemap: https://skribra.com/sitemap.xml
- Public REST API: https://skribra.com/api/v1
- OpenAPI specification: https://skribra.com/openapi.json