Overnight, all AIs can "speak Mandarin"! How powerful is Google's A2A agreement?
The revolutionary moment of AI interconnection has arrived! How does Google's A2A protocol reshape the collaboration of intelligent agents?
Core content:
1. Google's A2A protocol: breaking the communication barriers between AI agents
2. A2A and MCP protocols: comparative analysis of target positioning, technical architecture, and application scenarios
3. How A2A and MCP complement each other and jointly promote the AI infrastructure revolution
If AI is likened to the new spark of human civilization, then April 2025 is destined to be the critical moment to ignite the spark .
Google has just joined forces with 50 giants to release the A2A protocol , while the MCP protocol led by Anthropic has already penetrated the front line of AI development.
These two protocols, like TCP/IP and HTTP in the early days of the Internet , are laying the foundation for the interconnection and interoperability of AI agents.
But ordinary users may wonder:
Why are there two protocols? Are they competing or complementary?
Below, we will use the most common metaphors and the most practical cases to help you understand this epoch-making AI infrastructure revolution .
Google released the Agent-to-Agent ( A2A ) open protocol at the Google Cloud Next conference on April 10. The core of the A2A protocol is to provide a standardized communication framework for AI agents developed by different developers or manufacturers , enabling them to cross platform , system or ecological barriers, securely exchange information and collaborate to complete tasks.
A2A Workflow
Different AI agents declare their capabilities through " Agent Cards ". The client sends structured tasks on demand, and the server synchronizes progress in real time and processes multimodal data (text/image/video streams), ultimately delivering the results securely.
A2A workflow diagram
A2A ( Agent-to-Agent ) protocol and MCP ( Model Context Protocol ) are both open protocols in the field of artificial intelligence. Their core differences are reflected in target positioning, technical architecture and application scenarios .
1. Target positioning
A2A: Focus on collaboration between agents
The core of the A2A protocol is to solve the communication and collaboration problems between different AI agents, aiming to break the ecological isolation and enable agents developed by different frameworks and suppliers to work together like team members.
For example, the inventory management agent in the e-commerce scenario can call the logistics agent and payment agent to complete order processing.
MCP: Focus on connecting models with external resources
The MCP protocol was proposed by Anthropic, and its core is to standardize the interaction between large language models (LLMs) and external data sources and tools.
For example, with MCP, models can securely access enterprise databases or call APIs without having to develop separate interfaces for each tool.
An analogy between the two:
A2A is the " diplomatic protocol of AI " that solves the " dialogue " problem between intelligent agents
MCP is the " universal interface of AI " that solves the " connection " problem between the model and the outside world
2. Technical Architecture
3. Application scenarios
Typical A2A scenarios
Cross-system collaboration:For example, enterprises integrate agents from platforms such as Salesforce (CRM) and Workday (HR) to automatically process customer requests.
Complex Task Decomposition: Medical ScenarioIn the process, the image analysis agent and the medical record analysis agent collaborate to diagnose
Real-time interaction: autonomous drivingVehicle Agent communicates with traffic facility Agent to avoid risks
Typical scenarios of MCP
Data Augmentation:LLM accesses the real-time database through MCP and generates reports based on the latest data
Tool call: AI customer service calls the calendar API to schedule meetings, or accesses the file system to organize documents
Multi-model adaptation: Developers can switch between different LLMs (such as Claude and GPT-4) through the MCP unified interface.
4. Synergistic relationship
The two are not competing, but complementary:
Collaboration process:
A2A is responsible for coordinating the division of labor among multiple agents , while MCP provides external resource support for a single agent.
For example, the travel planning agent calls the flight booking agent through A2A, and the latter accesses the airline API through MCP to obtain real-time ticket prices.
Complementary technologies:
A2A solves " horizontal expansion " (more agents collaborating), while MCP solves " vertical expansion " (increased capabilities of a single agent)
When MCP gives AI " hands and feet " and A2A gives AI " social capabilities ", a true era of intelligent collaboration has arrived.
This is not only a technological advancement, but also a paradigm shift in the way humans organize themselves. The future competitiveness of enterprises may depend on the " collaborative IQ " of their AI teams.
