Read More《在中国做AI难，做AI Agent容易》
Positive Comments: AI Agent – A Pragmatic Path and Value Leap for China’s AI Industry to Break Through
In 2025, China’s AI industry is showing a distinct differentiation of “difficult breakthroughs in the basic layer and vitality in the application layer.” While the world is still troubled by the high costs, technical barriers, and supply – chain risks of large – model R & D, the rise of China’s AI Agent (Artificial Intelligence Agent) has found a more practical “breakthrough path” that suits the local reality for the industry. Its value is not only reflected in the implementation efficiency of technological applications but also in the in – depth empowerment of China’s digital economy ecosystem and the new paradigm of “application – driven innovation” provided for the global AI industry.
I. The Value Leap from “Tool” to “Intelligent Agent” Hits the Core Pain Points of Enterprises
The core breakthrough of AI Agent lies in its ability to “solve problems” rather than the traditional AI tools’ ability to “answer questions.” The e – commerce operation review scenario mentioned in the news is a microcosm of the daily workflow of enterprises: a large number of repetitive, cross – system information integration and execution tasks consume a lot of employees’ time. Through the closed – loop of “perception – decision – action – learning,” AI Agent connects these discrete work links into an automated process and directly delivers business results (such as meeting reports and meeting arrangements). In essence, it upgrades “Software as a Service (SaaS)” to “Results as a Service (RaaS).” This model highly aligns with the core demand of enterprises for “cost reduction and efficiency improvement.” Especially in China’s highly competitive business environment, enterprises’ demand for efficiency improvement is almost a “rigid need,” so the market acceptance and willingness to pay for AI Agent are naturally higher.
II. China’s Unique “Scenario Dividend” and “Infrastructure Advantage” Build a Fertile Ground for the Development of AI Agent
The advantage of China in developing AI Agent is essentially an extension of the “first – mover advantage in the digital economy.” First of all, China has the world’s most complex and active digital application scenarios: from e – commerce and food delivery to industrial manufacturing and smart cities, almost all industries have completed in – depth digital transformation. This “scenario density” provides a rich “training ground” for AI Agent. An e – commerce AI Agent that can switch freely between Taobao, Douyin, and ERP systems has far stronger adaptability than similar products trained in a single and simple scenario. Secondly, the complete digital infrastructure (mobile payment, cloud services, API ecosystem) supports the “end – to – end” task closed – loop of AI Agent. For example, a travel AI Agent can easily complete the entire process of “planning – booking – paying – checking in” in China, while in countries with an imperfect payment system, this closed – loop may not be realized due to the “payment breakpoint.”
III. The “Application – Driven Innovation” Culture Lowers the Technical Threshold and Accelerates Commercialization
Different from the original innovation of “from 0 to 1” required for basic large – model R & D, AI Agent relies more on the scenario adaptation ability of “from 1 to 100.” The “pragmatism” gene of Chinese technology enterprises happens to meet this demand – instead of being obsessed with inventing the “sharpest hammer,” they quickly find the “most suitable nail.” By using open – source models or commercial APIs (such as GPT – 4, Tongyi Qianwen) as the technical base and fine – tuning them according to local scenarios, enterprises can launch available AI Agent products at a lower cost and faster speed. This model of “technical base + scenario innovation” not only avoids the high – investment risks in the basic layer but also quickly verifies the commercial value. Currently, giants such as Alibaba and Tencent have integrated Agent into business ecosystems such as e – commerce and finance, while vertical – field enterprises such as Zhongke Shiyu (transportation) and Zhuoshi Technology (medical) have created differentiated Agent products by deeply exploring industry pain points, further verifying the feasibility of this path.
IV. The Dual Drive of Policy and Market Opens up the Industry Growth Space
The Chinese government’s support for the implementation of AI applications provides the “policy impetus” for AI Agent. From the “Artificial Intelligence +” initiative to the construction of the “digital government,” policies clearly encourage the integration of AI with the real economy, which directly stimulates the demand for Agent in fields such as government affairs, medical care, and industry. For example, the “intelligent approval Agent” in digital government affairs can automatically process enterprise qualification reviews, and the “quality inspection Agent” in industry can analyze production line data in real – time. The commercialization potential of these scenarios is huge. At the same time, the market’s urgent demand for “digital productivity” (such as small and medium – sized enterprises’ eagerness for low – cost automation tools) further expands the growth space of AI Agent.
Negative Comments: Potential Hidden Concerns and Challenges under the AI Agent Craze
Although AI Agent shows vigorous vitality in China, its development is not without risks. From technical dependence to data security, from homogeneous competition to user acceptance, multiple challenges, if not handled properly, may restrict the long – term and healthy development of the industry.
I. Risks of Dependence on the Technical Base: Hidden Dangers behind the Convenience of “Using Models”
The prosperity of China’s AI Agent depends to a certain extent on the use of open – source models or commercial APIs. Although this “outsourcing of the technical base” model lowers the R & D threshold, it also poses potential risks. On the one hand, if the international environment changes and API interfaces are restricted (such as the access restrictions of some large models to Chinese enterprises), Agent products that rely on external models may face a “supply cut – off” crisis. On the other hand, the performance differences of different models (such as reasoning accuracy and response speed) will directly affect the quality of Agent’s task completion. If enterprises lack the in – depth adaptation ability to the underlying models, the product experience may be unstable. In addition, excessive dependence on external models may also lead to the “path lock – in” of technological innovation – enterprises will invest more resources in scenario development rather than core algorithm optimization, which may weaken the independent technological ability in the long run.
II. Data Security and Privacy Compliance: The “Sword of Damocles” in Complex Scenarios
The core ability of AI Agent is “cross – system invocation and data integration,” which means it needs to frequently access enterprise internal databases, user privacy information (such as health data and consumption records), and external public data (such as competitor information). The cross – platform flow of data greatly increases the risks of privacy leakage and data abuse. For example, if a medical AI Agent fails to encrypt patient medical record data during invocation, it may lead to the leakage of sensitive information. When an e – commerce AI Agent crawls competitor data, if it fails to comply with the anti – unfair competition law, it may cause legal disputes. Although China has issued the “Data Security Law” and the “Personal Information Protection Law,” the “autonomous decision – making” feature of AI Agent (such as automatic data crawling, analysis, and storage) brings new challenges to compliance supervision – how to define the responsible subject of Agent’s behavior (the enterprise or the developer) and how to ensure the “minimum necessary” principle of data use are still unsolved problems.
III. Homogeneous Competition and Pseudo – Demands in Scenarios: The “False Fire” behind the Prosperity
Behind the “flourishing” of the current AI Agent track, there are risks of homogeneous competition. Many enterprises choose to flock to “popular scenarios” such as e – commerce and customer service in order to quickly implement their products, but they fail to deeply explore the unique needs of the industry. For example, some e – commerce Agents only take “automatically generating promotional copywriting” as the core function, which has limited differences from traditional marketing tools. Some vertical – field Agents only stay at the level of “automated report generation” and fail to truly solve the “bottleneck” problems in business processes (such as supply – chain optimization and user demand prediction). In addition, some enterprises blindly package “pseudo – demand” scenarios (such as the “intelligent pet companion Agent”) in pursuit of “concept popularity.” The actual commercial value of these scenarios is limited, which may lead to resource waste.
IV. User Acceptance and Ethical Challenges: The Trust Gap from “Tool” to “Partner”
The “autonomous decision – making” feature of AI Agent upgrades it from a “passive tool” to an “active intelligent agent,” which places higher requirements on user trust. For example, enterprise managers may have doubts about the process of “AI Agent autonomously booking meeting rooms and sending invitations.” If the Agent misjudges the scope of participants, it may cause internal communication chaos. Ordinary users may lack trust in the diagnostic suggestions of the “AI family doctor” and worry about its professionalism and liability attribution. In addition, the “learning and memory” module of AI Agent may lead to the accumulation of “algorithmic bias” (such as user portrait deviation based on historical data), which may then lead to discriminatory or unfair decisions. How to establish user trust through transparent design (such as displaying decision – making logic) and human – machine collaboration mechanisms (such as manual confirmation at key steps) is a key obstacle to the popularization of AI Agent.
Suggestions for Entrepreneurs: Seize the Opportunities, Avoid Risks, and Take the “Pragmatic + Innovative” Path
For entrepreneurs who are involved in the AI Agent track, the current situation is both a “window period” and an “elimination round.” Based on the industry status quo and potential challenges in the news, the following suggestions are worth considering:
I. Deeply Explore Vertical Scenarios and Dig out “Real Demands” Instead of “Pseudo – Hot Spots”
Avoid blindly following popular scenarios (such as general – purpose customer service Agents). Instead, focus on the “rigid pain points” of specific industries. For example, the “equipment predictive maintenance Agent” in the industrial field (which predicts faults in advance by analyzing sensor data) and the “personalized learning path planning Agent” in the education field (which dynamically adjusts the learning plan based on students’ answer data). The demands of these scenarios are clear, the willingness to pay is strong, and the competition threshold is relatively high (requiring industry knowledge accumulation). Entrepreneurs need to conduct in – depth industry research and jointly define the demands with enterprise users to ensure that the Agent solves problems that “must be solved” rather than “optional” ones.
II. Balance Technical Dependence and Independent R & D to Build “Differentiated Barriers”
Although using external model APIs can quickly verify products, in the long run, enterprises need to gradually accumulate “scenario adaptation ability” and “small – model optimization technology.” For example, for Agents in the medical scenario, they can be fine – tuned based on open – source large models and incorporate medical professional corpora to improve the accuracy of diagnostic suggestions. For Agents targeting small and medium – sized enterprises, a lightweight reasoning engine can be developed to reduce the dependence on high – computing – power hardware. At the same time, pay attention to the development of domestic large models (such as Deepseek, Tongyi Qianwen) and make early arrangements for adaptation to reduce the risk of “technical supply cut – off.”
III. Strengthen Data Security and Privacy Compliance to Build a “Trustworthy Agent”
Data security is the “lifeline” of AI Agent. Entrepreneurs need to establish strict data management processes. In the data collection stage, clearly define the scope of user authorization and abide by the “minimum necessary” principle. In the data processing stage, use technologies such as federated learning and differential privacy to protect sensitive information. In the data storage stage, prevent leakage through encryption and permission classification. In addition, actively study relevant regulations (such as the “Interim Measures for the Management of Generative AI Services”) to ensure that the “autonomous decision – making” behavior of the Agent complies with legal requirements (such as complying with the robots protocol when crawling public data).
IV. Design a “Human – Machine Collaboration” Mechanism to Lower the User Trust Threshold
The “autonomy” of AI Agent needs to be balanced with “controllability.” For example, set up manual confirmation links at key decision – making nodes (such as meeting invitation scope and financial reimbursement approval). Use the “decision – making log” function to show users the reasoning process of the Agent (such as “Due to a 20% decline in sales data, it is recommended to invite regional managers to the meeting”). For B – end users, provide “customized training” services (allow enterprises to fine – tune the Agent model with their own data) to enhance their confidence in the Agent’s ability.
V. Pay Attention to Industry Standards and Ecosystem Cooperation to Avoid “Working in Isolation”
The development of AI Agent requires cross – industry collaboration. Entrepreneurs can actively participate in the formulation of industry standards (such as the ability evaluation indicators and data interface specifications of Agents), cooperate with cloud service providers (such as Alibaba Cloud and Tencent Cloud) and API platforms (such as the DingTalk Open Platform) to share technological resources, and collaborate with industry associations (such as the China Artificial Intelligence Industry Development Alliance) to obtain policy support and market information. Through ecosystem cooperation, enterprises can not only reduce development costs but also improve the compatibility and universality of products.
Conclusion
The rise of China’s AI Agent is the result of the resonance of technology, scenarios, and policies, and it is also a successful practice of the “application – driven innovation” model. It has not only found a more practical development path for China’s AI industry but also provided a “Chinese sample” for global AI applications. For entrepreneurs, seizing the scenario dividend, avoiding technological risks, and building user trust are the keys to “breaking through” in this track. In the future, with the in – depth integration of AI Agent and the real economy, its value may be upgraded from an “efficiency tool” to an “industrial intelligent hub,” becoming the core engine driving the high – quality development of China’s digital economy.
