Entering the era of "swarm intelligence", MCP and A2A are subverting the AI ​​ecosystem

In March and April 2025, the AI ​​technology circle is boiling!

We are witnessing a historic turning point from "individual intelligence" to "group intelligence", and the AI ​​industry landscape will also undergo major changes.

Two revolutionary protocols: Model Context Protocol (MCP) and Agent-to-Agent Protocol (A2A) . These two technologies not only redefine the way AI systems interact, but also herald a new era dominated by connected intelligent agents . This article will use easy-to-understand cases to analyze how they subvert the traditional AI ecosystem and reshape the future technology landscape.

• • MCP is promoted by giants such as Anthropic and Microsoft and is becoming the first choice for enterprise-level integration.
• • A2A relies on Google’s open source ecosystem, attracting many developers to build flexible multi-agent systems.

What are MCP and A2A?

MCP (Model Context Protocol)

• •  Core function : As a "universal adapter" for AI and external tools and data sources. Developers do not need to develop interfaces for each tool separately, but only need to call databases, APIs, file systems and other resources uniformly through MCP. MCP has two operating modes, one is SSE (remote streaming output) and the other is STDIO (local standard output)
• •  Example : When a customer inquires about the order status from the intelligent customer service, the customer service AI directly connects to the enterprise order database through MCP, retrieves relevant order information, and provides the customer with an accurate order status report without human intervention. In this scenario, MCP acts as a technical bridge between AI and the enterprise backend database (the interface for querying orders behind the scenes has been encapsulated into MCP Server).

A2A (Agency to Agency Agreement)

• •  Core functionality : Enables different AI agents to collaborate like humans. They can dynamically discover each other’s capabilities, negotiate the division of tasks, and securely pass data.
• •  Example : When a customer needs to modify the delivery address after placing an order, the customer service AI agent receives this request and determines that support from the logistics system is required. Through the A2A protocol, it establishes communication with a dedicated logistics AI agent that has the authority to modify delivery information. The two AI agents collaborate to solve the problem in their respective areas of expertise, forming a complete service process.
• The author drew a picture, which can clearly see the difference between them.

From “Tool Calling” to “Swarm Intelligence”

If you are still stuck in learning Function Call of large models, I am sorry to tell you that it is outdated!

Traditional AI systems are isolated “single intelligence”, while MCP and A2A break down barriers in the following ways:

MCP: Vertically Expanding AI Capabilities

• •  Tool call standardization : In the past, AI calls to different tools required customized interfaces, which was time-consuming and labor-intensive. MCP unifies the input and output formats, so developers only need to integrate once to connect to all tools that support MCP.
• •  Case study : Medical diagnosis AI simultaneously accesses electronic medical records, laboratory data, and medical literature libraries through MCP to generate comprehensive diagnostic recommendations.

A2A: Building a collaborative network horizontally

• •  Dynamic collaboration replaces hard-coded processes : Traditional multi-AI collaboration requires pre-definition of interaction logic, while A2A allows agents to autonomously discover and negotiate at runtime to form flexible task chains.
• •  Case study : In enterprise IT incident response, log analysis AI, security detection AI, and notification AI are dynamically teamed up through A2A to adjust the processing flow in real time based on the incident type without the need for human intervention.

Ecological Effects of Technological Complementarity

MCP and A2A are not competitors, but rather  the “dual engines” that form the AI ​​ecosystem :

• •  Underlying capabilities (MCP) : Provide rich tool support for individual AI and enhance its vertical field expertise.
• •  Collaborative Framework (A2A) : Connect multiple professional AIs into a network to solve complex problems.
• •  Future architecture : For example, logistics AI accesses GPS data through MCP, and then collaborates with warehousing AI and distribution AI to optimize routes through A2A.

The real dilemma of the two protocols

Challenges facing A2A

Despite its elegant design, A2A still faces challenges:

• • Complexity management: Multi-agent networks can lead to a dramatic increase in interaction complexity.
• • Efficiency issue: Frequent communication between agents may affect the overall efficiency.
• • Security and governance: The open intelligent agent collaboration model increases security risks and governance difficulties.
• • Limited model support: Currently only Google’s own Gemini is explicitly supported, and the level of support from other large model vendors is not yet clear.

MCP is not as perfect as imagined:

• • Limited collaboration capabilities among agents: MCP focuses mainly on the interface between models and applications, and provides insufficient support for collaboration among agents.
• • Insufficient flexibility: Standardized interfaces may limit the implementation of some innovative application scenarios.
• • Implementation complexity: Fully supporting the MCP specification requires considerable effort.
• • MCP may face “tool poisoning attacks”, where malicious instructions are hidden in the tool description to hijack AI behavior.
• • Enterprise-level use of MCP faces the problem of identity authentication, and the official SDK has not yet been provided.

A paradigm revolution in software architecture

MCP and A2A mark the transition from  "deterministic programming"  to  "adaptive systems  ." Traditional software relies on hard-coded processes, while future systems will be composed of autonomous and collaborative intelligent networks with the ability to dynamically discover, negotiate in real time, and self-optimize .

Just as the TCP/IP protocol defines the Internet, MCP and A2A may become the cornerstone of communications in the AI ​​era . If enterprises can grasp this trend, they will be the first to build a truly intelligent and flexible technology ecosystem and usher in a new era of "group intelligence".