From "Selling Hardware" to "Intelligent Co - creation": The "Awakening of the Intelligent Entity" of an AIoT Leader
The security inspection of a factory in Tongxiang is undergoing an "efficiency revolution" brought about by intelligent agents. Intelligent agents are embedded in every aspect of daily patrols: video patrols analyze real-time images of the factory buildings to accurately locate abnormalities; the intervention of digital assistants helps safety officers plan patrol routes and provides suggestions for problem handling; at the same time, with the help of the "Everything Search" function, historical records can be quickly retrieved to assist in decision-making; finally, during the shift handover process of safety officers, the system can automatically generate reports to achieve a quick handover. Ultimately, while improving the handling of emergencies, one safety officer has replaced the work of the original quality inspectors and security guards, achieving a leap in efficiency.
This is a real inspection scenario at UniVista's Tongxiang factory, as described by Zhu Bing, the Chief Product Officer of UniVista Technology, at the 2025 UniVista Partners Conference. "Currently, the development of AIoT has entered the 3.0 era. The new open-source paradigm of DeepSeek has promoted the equalization of industry large models and the emergence of intelligent agents. What large model technology has reconstructed is not only algorithms but also the further sinking of cloud capabilities to the edge." Zhu Bing summarized.
If last year's UniVista Partners Conference focused on "equipping with large models," emphasizing the optimization of the artificial intelligence technology foundation, then this year's conference's attention to intelligent agents empowering AIoT reflects UniVista's comprehensive integration of AI capabilities at the application end: abstract technologies such as large models, edge computing, and hardware equipment have been materialized into implementable "productivity units" by the newly launched full-link intelligent agent products at the conference.
The "Intelligent Agent Revolution" in the New Era of AIoT
The efficiency revolution in the security inspection of the Tongxiang factory is a microcosm of the AIoT industry's leap into the 3.0 era: driven by large models as the core, the equalization of industry technologies has given rise to the emergence of intelligent agents, which in turn promotes the intelligent upgrade of AIoT to a more efficient and low-cost cloud-edge-device collaborative model, empowering multiple scenarios such as industry and security.
The landmark event marking the beginning of this era was the open-sourcing of CV large models, which significantly improved the development efficiency of long-tail algorithms for machine vision on cloud platforms. This year, the new open-source paradigm of DeepSeek has further deepened the transformation of AIoT: the combination of the new generation of GPUs and AI-ASIC chips has made cloud-edge-device collaborative computing the mainstream, and intelligent agents have thus become the hottest keyword in the industry.
In a broad sense, an intelligent agent is an autonomous system evolved from large model technology. Specifically for AIoT intelligent agents, Zhu Bing gave UniVista's definition: it is an intelligent entity that can perceive the environment, form memories, think, execute tasks, and continuously evolve itself; the specific forms include digital intelligent agents, embodied intelligent agents, and spatial intelligent agents.
The level of decision-making and execution ability determines the intelligence level of an intelligent agent. Zhu Bing compared it to the grading of autonomous driving and divided the intelligence level of intelligent agents into levels L1 - L5.
In his view, level L2, the assisted execution level, is currently initially mature. AIoT can identify visual data and conduct semantic interactions to assist in tasks. Level L3 - automatic assisted execution is still under development. After development, AIoT intelligent agents can further autonomously evolve their recognition and understanding abilities, make partial planning decisions based on artificially established rules, and assist in the automatic execution of simple tasks.
Behind the leap in AIoT intelligence is not only a simple technological upgrade but also a reconstruction of the entire production relationship. Traditional IoT was limited by computing power and algorithms, forming a "centralized star structure" - data was aggregated and processed in the cloud, and entities were simply connected. In the era of large models, the equalization of computing power and algorithms has enabled each entity to evolve into an intelligent agent, and the connection network has transformed into a decentralized "full-mesh structure."
Zhang Pengguo, the President of UniVista, summarized the characteristics of this full-mesh structure: intelligent agents interact autonomously to complete tasks based on complementary capabilities and collaborate equally; natural language understanding eliminates protocol barriers, enabling seamless collaboration between cross-brand devices; the contribution of a single node's capabilities is catalyzing a global leap in intelligence, and an intelligent ecosystem is initially emerging. "In short, due to changes in technology and organization, the evolution of connection methods, and the disappearance of time, space, and language barriers, all industries will undergo significant changes. The arrival of the singularity of human-like intelligence makes it possible for all industries to restart and everything to become intelligent."
UniVista Builds the "Hardcore Body" of Intelligent Agents
It can be seen that the core logic of the intelligent agent revolution lies in: the equalization of large models and cloud-edge-device collaboration have reconstructed the underlying technological paradigm of AIoT, but the abstract nature of technological concepts must be truly implemented through "tangible hardware" and "reusable technological architectures." At this year's Partners Conference, UniVista presented its specific implementation plan: the "Wutong 2025" AIoT large model and the full-link intelligent agent product Agent Link.
The Wutong model has evolved over time, and its evolution history essentially reveals the underlying logic of AIoT's transformation from fixed functions to dynamic evolution. The Wutong 2.0 released last year solved the problems of long-tail algorithm development efficiency and low-light imaging, laying the foundation for "equipping with large models." With the Wutong 2025 AIoT large model, on top of the original two-layer structure of the general model + industry model, a task model has been added, enabling support for the perception, thinking, evolution, and dialogue capabilities of AIoT intelligent agents, thus achieving a complete construction of the foundation for AIoT intelligent agents.
The pain point of traditional AIoT lies in the "impossible triangle" of computing power, cost, and efficiency. UniVista has explored a practical solution in practice: in terms of technological logic, the multi-modal large model is disassembled across the cloud, edge, and device - the device side focuses on visual perception, the edge side aligns multi-modal data, and the cloud side strengthens language logic, increasing the computing power utilization rate by 40%; in terms of business logic, through model distillation and hardware customization, the price of edge domain products is 20% lower than the industry average, solving the problem of deployment costs in small and medium-sized scenarios.
The full-link products above the underlying architecture truly implement the underlying technological paradigm and concepts into "productivity." It can be said that this series of products are not isolated functional units but rather capability modules for ecological collaboration.
The perception end includes cameras with a five-fold improvement in night vision capabilities, 60GHz millimeter-wave radar alarm devices, etc., to solve the adaptability in complex environments; the radar-vision fusion capture device is suitable for vehicle-road collaboration, extending traffic management to urban digital twins. One of the representative products in the edge domain, the "Kunlun" all-in-one machine with 1024T computing power, meets high-concurrency requirements. Domestic storage and computing reduce the dependence on information technology innovation, allowing government and enterprise customers to choose according to their needs. The cloud capabilities support the "Everything Search" function of searching for images by text and images, transforming data from resources into assets and assisting in decision-making in scenarios such as emergency command and intelligent operation and maintenance.
Overall, through a diverse combination of hardware products and powerful large model application capabilities, UniVista has launched a complete set of intelligent solutions to help solve a series of industry pain points such as computing power allocation, cost control, imaging quality, device intelligence, and local adaptation.
This practice represents two major trends in the intelligent development of AIoT to a certain extent. The first is the ecologicalization of hardware intelligence. Hardware devices such as cameras, access control systems, and alarms are no longer isolated terminals but the "hardcore body" of intelligent agents, with functions dynamically upgraded as the model iterates. The other trend is the breakthrough in domestic substitution. From chip adaptation to system integration, UniVista's practice once again proves that domestic substitution is not only a necessary choice in the current geopolitical environment but also the optimal business solution in terms of cost and security.
Business Scenario Fission, Realizing Intelligence for Everything
Through the Wutong AIoT intelligent agent foundation and the full-scenario hardware matrix, UniVista has constructed the "sensory - neural - brain" system of intelligent agents. However, the ultimate goal of technological empowerment is not to pile up indicators and parameters but to make intelligent agents truly integrate into the capillaries of all industries and transform into real "productivity." To this end, UniVista has proposed a full-scenario intelligent business from the user's perspective, namely the Everything X intelligent business and digital assistant interaction experience covering four main scenarios: command and monitoring, data analysis, operation and maintenance management, and mobile inspection. In essence, this solves the technical feasibility problem of "how to use."
Overall, the full-scenario intelligent business architecture is supported by a "1 + 8 + 4" system, that is, one digital assistant interaction platform serves as the human-machine collaboration center, eight everything interconnection scenarios build vertical domain solutions, and four intelligent centers form a closed-loop service capability.
Among them, the digital assistant platform breaks through the traditional system interaction logic and realizes core functions such as natural language interaction, pre - plan recommendation, and task scheduling based on large model technology. By opening up the custom interface for the image, the intelligent agent becomes an organic part of the enterprise's digital twin.
The everything interconnection scenarios include Everything Display, Everything Patrol, Everything Control, Everything Verification, Everything Search, Everything Description, Everything Maintenance, and Everything Diagnosis. In these eight scenarios, UniVista has improved the efficiency of traditional security operations by a hundredfold through technological innovations such as intelligent analysis, REID tracking, and self - learning algorithms - video screening has been shortened from 20 hours to 1 minute, and target tracking has been compressed from 3 days to 10 minutes, achieving a qualitative leap from physical security to data "intelligence."
The four intelligent centers have constructed a complete value realization path: the command and monitoring center reduces costs in the operation process through intelligent scheduling; the data analysis center precipitates reusable industry knowledge graphs; the operation and maintenance management center realizes self - diagnosis of faults; the mobile inspection center constructs a minute - level response system. This closed - loop architecture from perception to decision - making to execution essentially reconstructs the operation paradigm of traditional security, transforming the discrete device network into an organic intelligent ecosystem.
Perhaps the more strategically valuable breakthrough lies in the new AIoT ecosystem intelligent agent co - creation model proposed by UniVista. By opening up the capabilities of the Wutong large model, it empowers partners to create intelligent agents in vertical domains, forming a solution matrix covering multiple industries. In terms of building a business closed - loop, UniVista has proposed a model that can be summarized as "intelligent agents as a service": in the pre - sales stage, VR digital twin technology is used to improve the efficiency of on - site surveys; in the sales stage, a no - code tactical platform is constructed to precipitate industry know - how; in the after - sales stage, an AIoT certification system is used to cultivate an industrial talent echelon. This closed - loop ecosystem not only lowers the technological threshold for small and medium - sized enterprises but also opens up a sustainable profit path through knowledge services, achieving a two - way value flow of "technological empowerment + experience sharing."
So far, UniVista's AIoT intelligent agent business blueprint has been fully unfolded, and the essence of this strategy is to build a new type of productivity tool. Different from the traditional piling up of software and hardware, UniVista is achieving natural interaction through digital assistants, deconstructing industry knowledge with the help of large models, and activating long - tail scenarios through the ecological network, ultimately achieving the target value of "intelligent agents as productivity." This transformation from technology - centered to value - centered is reshaping the consensus in the AIoT 3.0 era.
From the transformation of equipping with large models to the intelligent agent ecosystem, UniVista has not only achieved its own technological upgrade and business expansion but also reflects a facet of the new development direction and paradigm of the AIoT industry in the future - through underlying innovation, technological equalization, scenario breakthroughs, and ecological co - creation, it promotes the in - depth integration of AI and the real - world industries, helping all industries move towards a new intelligent era. As emphasized in the "Artificial Intelligence +" strategy of the 2025 Two Sessions, China will move from a "parallel runner" to a "leader" in the global AI field.