Agent2Agent protocol explained: Google leads the way in creating a universal AI agent communication standard

I read the official documentation as soon as possible. Now I will try to explain the key points of this protocol to you in a "as popular but technically reliable" way.

✨Official address: https://google.github.io/A2A/#/

The Agent2Agent (A2A) protocol is a standard protocol led by Google and jointly built by the open community. Its design goal is to allow AI agents built by different vendors using different frameworks (LangChain, CrewAI, OpenAgents, etc.), running on different clouds, to collaborate seamlessly and work as a team.

This standard was not developed by Google alone, but by more than 50 technology and consulting companies, including:

• Technology vendors such as Atlassian, MongoDB, LangChain, SAP, and Salesforce;

• Traditional consulting giants such as Accenture, BCG, PwC, and KPMG.

• MCP: Providing Tools
• A2A: Proxy to Proxy Connections

After reading the protocol document, I summarized the following keywords (very interesting, basically each of them is related to the pain points of real enterprise applications):

• Cross-platform collaboration
  

• No shared memory, context, or resources required
  

• Agents can communicate flexibly and task-centrically

• Building on existing standards
  

• Based on widely supported standard protocols such as HTTP, SSE, JSON-RPC, etc.
  

• Easy to integrate into existing IT infrastructure

• Support long-term tasks + real-time status updates
  

• Can handle task execution from a few seconds to a few days
  

• Support asynchronous feedback and streaming information return

•  Multimodal data transmission
  

• In addition to text, it also supports the intercommunication of audio/video/Web UI data

This shows that future AI collaboration is likely to be "multimodal collaboration" rather than "pure conversational workflow."

4. How does A2A work?

A2A facilitates communication between a "client" agent and a "remote" agent. The client agent is responsible for formulating and communicating tasks, while the remote agent is responsible for processing those tasks in an attempt to provide the correct information or take the correct action. This interaction involves several key functions:

Function Discovery

Each agent describes its capabilities by publishing an "Agent Card" in JSON format, which makes it easier for other agents to automatically discover and select appropriate partners.

Task Management

The core communication unit between agents is "Task". Each task has a complete life cycle and can update its status in real time. The execution result of a task is called  an Artifact .

Collaboration

Agents achieve dynamic collaboration by exchanging context information, task status, component content, or user instructions through messages.

UX Negotiation

Each message can contain multiple modules, representing specific content (such as text, images, iframes, etc.). Through these modules, agents can negotiate on format, display method, etc., to improve the user interaction experience.

The following are typical applications of A2A in real-world scenarios:

Scenario: Companies want to automate the recruitment process through AI

process:

• Recruiters enter job requirements (such as technology stack, location, etc.) in Agent space

• The master agent collaborates with multiple dedicated agents to complete candidate information collection and analysis

• Recommend candidates based on the analysis results and arrange interviews

• Call other agents to complete tasks such as background checks

  
  

These agents rely on A2A communication to complete the entire recruitment process. It sounds like "AI team remote collaboration" rather than one big model doing everything.

The Agent2Agent protocol is not reinventing the wheel, but standardizing the way existing fragmented agent systems connect . In the future, if AI agents really become part of enterprise infrastructure (like microservice systems), then protocols like A2A are likely to play the role of the "TCP/IP" layer.