An undercurrent of strategic maneuvering is sweeping through the AI landscape, centered on the standardization, protocols, and ecosystems that underpin artificial intelligence and intelligent agents.
Tech behemoths are deeply engaged in this silent but intense battle. Each strategic move and technological unveiling holds the potential to reshape the AI industry, reflecting a profound struggle for dominance and control over the future of AI and the allocation of its vast economic benefits.
The Colossus Conflict
While public attention is often drawn to the relentless competition in model parameters and performance metrics, a more consequential contest is unfolding behind the scenes.
In November 2024, Anthropic took a bold step by introducing the Model Context Protocol (MCP), an open standard for intelligent agents.
This initiative created significant ripples, aiming to establish a common language for interactions between large language models (LLMs) and external data sources and tools. It sought to create a universal system within the intricate world of AI interactions.
Anthropic’s move quickly resonated across the industry. OpenAI soon announced support for MCP in its Agent SDK, signifying a recognition of MCP’s value and a determination to remain competitive.
Google, a dominant force in technology, also joined the fray. Google DeepMind CEO Demis Hassabis confirmed the integration of MCP into Google’s Gemini model and software development kits, praising it as ‘rapidly becoming the open standard for the AI agent era.’
These endorsements from industry leaders rapidly amplified MCP’s influence, positioning it as a focal point in the AI domain.
However, the competition intensified. At the Google Cloud Next 2025 conference, Google unveiled the Agent2Agent Protocol (A2A), the first open-source standard for intelligent agent interaction. A2A eliminates barriers between existing frameworks and vendors, enabling secure and efficient collaboration among intelligent agents across different ecosystems. Google’s move demonstrated its technical prowess and innovative capabilities in AI, along with its ambition in building the AI ecosystem.
These actions by tech giants have brought the competition in AI and intelligent agents to the forefront, focusing on connection standards, interface protocols, and ecosystems. In a global AI landscape that is still evolving, the principle of ‘protocol equals power’ has become increasingly evident.
Whoever controls the definition of basic protocol standards in the AI era has the opportunity to reshape the global AI industry’s power structure and redistribute its economic benefits.
This extends beyond technical competition, escalating to a strategic game that will define future market structures and corporate growth.
AI Application ‘Connection Ports’
The rapid advancement of AI technology has resulted in the emergence of large language models (LLMs) like GPT and Claude, which showcase remarkable capabilities in natural language processing, text generation, and problem-solving.
These models’ potential lies in their ability to interact with external data and tools, addressing real-world challenges.
However, AI model interaction with the external world has been hindered by fragmentation and a lack of standardization.
The absence of unified standards and protocols forces developers to write specific connection code for each AI model and platform when integrating AI models with various data sources and tools.
To address these challenges, MCP was created. Anthropic compares MCP to a USB-C port for AI applications, emphasizing its versatility and simplicity.
Like the USB-C port, MCP aims to establish a universal standard that allows various AI models and external systems to use the same protocol, simplifying and streamlining AI application development and integration.
Consider a software development project. Before MCP, developers needed to write complex connection code for each code repository and AI model to analyze project code repositories using AI tools.
With MCP-based AI tools, developers can delve directly into project code repositories, automatically analyze code structures, understand historical commit records, and provide precise code recommendations based on project requirements. This improves development efficiency and code quality.
MCP consists of two main components: the MCP server and the MCP client. The MCP server acts as a data ‘gatekeeper,’ allowing developers to expose their data, whether from local file systems, databases, or remote service APIs.
The MCP client serves as an ‘explorer,’ building AI applications that connect to these servers for data access and utilization. The MCP server exposes the data, and the MCP client retrieves and processes it, creating a bridge between AI and the external world.
Security is essential when AI models access external data and tools. MCP standardizes data access interfaces, minimizing direct contact with sensitive data and reducing the risk of data breaches. Its built-in security mechanisms offer comprehensive data protection. Data sources can selectively share data with AI under strict security controls, and AI can securely relay results back to the data source.
For example, MCP servers can control resources without exposing sensitive information like API keys to large model technology providers. If a large model is attacked, the attacker cannot obtain this critical information, isolating risks and ensuring data security.
MCP’s advantages are evident in its practical applications and its value across various fields.
In healthcare, intelligent agents can connect to patient electronic medical records and medical databases via MCP, providing preliminary diagnostic suggestions based on doctors’ expertise.
In finance, intelligent agents can collaborate via MCP to analyze financial data, monitor market changes, and automate stock trading, making investment decisions more intelligent and efficient.
In China, technology companies such as Tencent and Alibaba have also responded by actively deploying MCP-related businesses. Alibaba Cloud’s Bailian platform offers full-lifecycle MCP services, simplifying the development process of intelligent agents and reducing the development cycle to minutes. Tencent Cloud has released the ‘AI Development Kit,’ which supports MCP plug-in hosting services, helping developers quickly build business-oriented intelligent agents.
Intelligent Agent Collaboration: A ‘Free Trade Agreement’
As the MCP protocol evolves, intelligent agents are transitioning from simple chatbots to action assistants capable of solving real-world problems. Tech giants are actively building their own standard and ecological ‘walled gardens.’ Unlike MCP, which focuses on connecting AI models with external tools and data, the A2A protocol aims for higher-level collaboration among intelligent agents.
The A2A protocol’s goal is to enable intelligent agents from different sources and vendors to understand each other and work together, granting greater autonomy and flexibility to multi-agent collaboration. This concept can be compared to the World Trade Organization (WTO), which aims to reduce tariff barriers between countries.
In the world of intelligent agents, different vendors and frameworks are like independent ‘countries,’ and the A2A protocol is like a ‘free trade agreement.’ Once adopted, these intelligent agents can join a ‘free trade zone,’ using a common ‘language’ to communicate and collaborate seamlessly, completing complex workflows that a single intelligent agent cannot handle alone.
Task management is a core component of the A2A protocol. Communication between clients and remote intelligent agents revolves around task completion. The protocol defines a ‘task’ object, which intelligent agents can complete quickly for simple tasks. For complex and long-term tasks, intelligent agents communicate to synchronize task completion status in real-time, ensuring smooth progress.
A2A also supports collaboration among intelligent agents. Multiple intelligent agents can send each other messages containing context information, replies, or user instructions, enabling them to work together to solve complex problems and complete challenging tasks.
Currently, the A2A protocol is supported by over 50 leading technology companies, including Atlassian, Box, Cohere, Intuit, MongoDB, PayPal, Salesforce, and SAP. Many of these companies have connections to the Google ecosystem.
For example, Cohere is an independent AI startup founded in 2019 by three researchers who previously worked at Google Brain. It has maintained close technical cooperation with Google Cloud for many years, with Google Cloud providing the computing power needed to train models. Atlassian, a well-known provider of team collaboration tools, has its Jira and Confluence tools widely used and collaborates with Google, with some applications available for use in Google products.
While Google claims that A2A complements Anthropic’s proposed MCP model context protocol, the commercial value of A2A is expected to continue to rise as more companies join, playing a leading role in the development of the intelligent agent ecosystem and driving industry change and advancement.
Open Collaboration or Ecological Division?
The competition between MCP and A2A highlights differing perspectives among tech giants regarding the AI industry’s value chain. Anthropic is building a ‘data access as a service’ business model through MCP, charging enterprise-level customers based on API calls to deeply integrate internal data assets with AI capabilities. Google relies on the A2A protocol to drive cloud service subscriptions, linking the construction of intelligent agent collaboration networks with Google Cloud computing power, storage, and other infrastructure, forming a closed-loop ecosystem of ‘protocol-platform-service.’
At the data strategy level, both demonstrate clear monopolistic intentions: MCP accumulates deep interaction data in vertical industries by deeply penetrating enterprise data cores, providing a rich source for customized model training; A2A captures massive amounts of process data in cross-platform collaboration, feeding back into Google’s core advertising recommendation and business analysis models.
Although both claim to be open source, their technical stratification strategies contain hidden mechanisms. MCP retains paid interfaces for enterprise-level functions, and A2A guides partners to prioritize access to the Google Cloud ecosystem. In essence, both are building technical moats through a model of ‘open-source infrastructure + commercial value-added.’
Standing at the crossroads of industrial transformation, the evolution paths of MCP and A2A are reshaping the underlying architecture of the AI world. On one hand, the emergence of standardized protocols is accelerating the process of technological democratization, allowing small and medium-sized developers to access the global ecosystem through unified interfaces, compressing the deployment cycle of enterprise-level applications from months to hours. On the other hand, if the protocol system led by giants forms a separatist regime, it will lead to an increased data island effect, high technical compatibility costs, and may even trigger zero-sum games in ‘ecological camps.’
A deeper impact lies in the intelligent penetration of the physical world: with the explosive growth of industrial robots, autonomous driving terminals, and medical intelligent devices, MCP and A2A are becoming the ‘neural synapses’ connecting virtual intelligence with the physical world.
In intelligent manufacturing scenarios, robotic arms synchronize operating condition data in real-time through standardized interfaces, AI models dynamically optimize production parameters, and build a closed-loop intelligence of ‘perception-decision-execution.’ In the medical field, the real-time collaboration of surgical robots and diagnostic models allows precision medicine to move from concept to clinical practice. The core of these changes is that the strategic value of protocol standards as ‘digital infrastructure’ is surpassing technology itself, becoming the key to unlocking a trillion-dollar intelligent economy.
However, challenges remain severe: the millisecond-level requirements for protocol real-time performance in industrial control and the stringent standards for privacy protection of medical data are forcing the continuous evolution of the protocol system.
When technological competition and commercial interests are deeply intertwined, the art of balancing openness and closure becomes critical. Perhaps only by establishing a cross-industry standard co-governance mechanism can we avoid repeating the mistakes of the ‘railway gauge war’ and truly realize the technical ideal of ‘Internet of Everything.’
In this silent power game, the contest between MCP and A2A is far from over. They are both products of technological innovation and carriers of commercial strategies, jointly writing a key chapter in the AI industry’s transition from ‘single intelligence’ to ‘ecological synergy.’
Ultimately, the direction of the industry is determined not only by technological advantages but also by value choices about openness, sharing, and ecological win-win, which is the most core ‘protocol standard’ of the AI era.