How do AI agents talk to each other? Agent-to-agent communication explained

Two open standards cover it. MCP lets an agent call tools and data sources. A2A (Agent2Agent), announced by Google in April 2025 and now a Linux Foundation project, lets one agent delegate work to another agent - often from a different vendor - with defined tasks, messages and results. Together they are how "agents working together" stops being marketing.

Why agents need to talk at all

Real business processes cross systems and vendors: your sales agent needs something from the logistics agent, which needs a check from the finance agent. Without a standard, every pair of agents needs a custom bridge, and multi-vendor setups stall. A2A defines the common language: agents publish what they can do (an "agent card"), accept tasks, report progress, and return results.

A2A and MCP are complements, not rivals

Google positioned A2A explicitly next to MCP: MCP connects an agent to tools and data; A2A connects agents to agents. A travel agent example: your planning agent uses MCP to read your calendar, then A2A to ask an airline's booking agent for options, then reports back with a choice for you.

Where it stands in 2026

A2A moved to the Linux Foundation in June 2025 as a neutral, open project with broad industry backing. The specification is public, and implementations are appearing across the major cloud and agent platforms. It is still young - treat it as the direction of travel, not a finished utility. For most SMEs the practical takeaway is not to build anything, but to prefer tools that speak the open standards when you buy.

The business caution

Agent-to-agent chains multiply the questions from single agents: who approved this action, under whose identity did that agent act, and where did the data cross a border? Swiss companies should apply the same discipline as with any outsourcing: know which agent did what, on whose authority, with which data.

Sources

Last updated: 20 October 2026.

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