# The A2A Protocol: How AI Agents From Different Vendors Learn to Talk to Each Other

> The A2A protocol lets AI agents from different vendors communicate. Here is how growth companies safely adopt Google's open standard for agent projects.

- Published: 2026-09-06
- Author: Master Mind
- Canonical: https://aimaster.fi/en/artikkelit/a2a-protokolla-nain-eri-valmistajien-ai-agentit-oppivat-puhumaan-toisilleen

When Google launched the Agent2Agent protocol (A2A) in April 2025, it arrived with support from more than 50 technology companies, including Salesforce, SAP, ServiceNow, and PayPal (Google Developers Blog, 2025). The protocol solves a problem that stalls many growth companies' agent projects: AI agents from different vendors cannot talk to each other.

Your sales agent has no idea what your finance agent is doing. Your customer service agent cannot ask your supply chain agent for help, because they share no common language. This is familiar territory once a growth company deploys agents from multiple vendors — one in the CRM, one in the ERP, one built in-house. Each agent ends up working alone in its own silo.

## What is the A2A protocol?

A2A (Agent2Agent) is an open protocol Google launched in April 2025. It gives AI agents built by different vendors and on different frameworks a shared way to communicate, securely exchange information, and coordinate tasks with each other. More than 50 technology companies support the protocol, including Salesforce, SAP, ServiceNow, PayPal, Atlassian, and MongoDB (Google Developers Blog, 2025). A2A is built on established standards — HTTP, SSE, and JSON-RPC — which makes it straightforward to connect to existing systems.

## How does A2A differ from MCP?

MCP (Model Context Protocol) connects a single AI agent to the tools and data it needs, such as files or databases. We cover MCP in more depth in our article on the [MCP protocol connecting AI agents to your systems](https://aimaster.fi/en/artikkelit/mcp-protokolla-yhdistaa-ai-agentit-jarjestelmiin-nain-kasvuyritys-ottaa-sen-kayt). A2A solves a different problem: it connects agents to each other. Google describes A2A as complementing Anthropic's MCP protocol, not replacing it (Google Developers Blog, 2025). Most growth companies will need both: MCP between an agent and its data, A2A between agents.

## Why does A2A matter for a growth company?

A growth company rarely buys agent technology from a single vendor. The CRM comes from one provider, finance software from another, and the in-house team builds a third agent for customer service. Without a shared protocol, these agents cannot delegate tasks to each other. A2A gives every agent a shared card — a JSON description of its own capabilities — that lets another agent find the right one for a task (Google Developers Blog, 2025).

## How does a growth company adopt A2A in practice?

Adoption happens in three steps. First, map where agent collaboration would create measurable value — that is [Master Plan](https://aimaster.fi/tuotteet/master-plan), an AI strategy sprint that maps where AI creates the most value for your company, measured in euros.

Second, build the data foundation. [Master Layer](https://aimaster.fi/tuotteet/master-layer) is a data foundation layer that connects your company's existing systems — CRM, ERP, documents — securely for AI use. This layer is a prerequisite: agents need to act reliably on trustworthy data before they start talking to each other.

Third, build the agents themselves. [Master Mind](https://aimaster.fi/tuotteet/master-mind) is a set of AI agents that runs on top of Master Layer's data and handles business processes independently. Building agents to be A2A-compatible from day one keeps a company from locking itself into a single vendor. Custom AI solutions are delivered in agile sprints: one sprint is 3 development days, so the first interoperable agent pair can be tested quickly.

|  | Traditional point-to-point integration | A2A-compatible agent network |
| --- | --- | --- |
| Adding a new agent | A separate integration for every pair | The agent publishes its capabilities; others discover it |
| Vendor dependency | Locks the company into one platform | Works across agents from different vendors |
| Maintenance load | Grows quickly with the number of agents | One shared standard for every agent |

A2A is backed by broad industry collaboration. More than 50 companies are involved in defining the protocol, including major service providers such as Accenture, Deloitte, KPMG, and PwC (Google Developers Blog, 2025). When this many software and consulting firms commit to the same standard, the odds of it becoming an industry norm rise sharply.

The first step isn't picking a protocol — it's mapping where agent collaboration would create measurable value in your company. A free [Master Mind analysis](https://aimaster.fi/analyysi) walks through where to start.

## Frequently asked questions

### What is the A2A protocol?

A2A (Agent2Agent) is an open protocol Google launched in April 2025 that gives AI agents from different vendors a shared way to communicate and coordinate tasks. More than 50 technology companies support it, including Salesforce, SAP, and ServiceNow (Google Developers Blog, 2025).

### How does A2A differ from MCP?

MCP connects a single AI agent to its tools and data. A2A connects agents to each other. Google describes the two protocols as complementary rather than competing standards (Google Developers Blog, 2025).

### Does a small growth company need A2A?

The need arises once a company runs agents from more than one vendor or built in-house. With a single agent, A2A adds no value yet. As the number of agents grows, a shared protocol prevents silos from forming.

### What does adopting A2A require from a company?

Adoption requires that a company's data is first made securely available to its agents. That is Master Layer's job. Only after that does it make sense to build multiple agents that communicate via A2A.

### How long does it take to build an A2A-compatible agent?

Custom AI solutions are delivered in agile sprints: one sprint is 3 development days. A first interoperable agent pair can typically be built and tested within a few sprints once the data foundation is in place.