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From July 17 to 20, the World Artificial Intelligence Conference (WAIC) 2026 in Shanghai once again served as a key window into the development of the global AI industry. At this year's WAIC 2026, one feeling stood out clearly: the narrative focus of the conference had quietly shifted.

If in previous editions the core topics were still the race for model parameters, competition in multimodal capabilities, and the technological spectacle of generative AI, then this year the most discussed topics became the engineering deployment of Agent architectures, the industrial application of embodied intelligence, and the commercialization validation of vertical-domain large models. This is not a simple rotation of tech hotspots, but a structural signal: AI is moving from the stage of technological innovation to the stage of industrial value realization.

In the past few years, we have witnessed the explosive evolution of foundation models, and large models have pushed AI to solve the problems of "understanding" and "generation." As model capabilities gradually mature, industries are beginning to focus on how AI can further enter business processes, participate in complex decision-making, and drive improvements in the operating efficiency of the real world.

Manufacturing is becoming a major landing point for this trend. As an industry with dense data, complex processes, and highly concentrated decision-making needs, manufacturing possesses rich industrial data and numerous high-value application scenarios. It is also becoming an important testing ground for AI's transition from technological innovation to industrial transformation.

AI Agents Move to the Industrial Frontline: Manufacturing Needs New Intelligent Collaboration Methods

At this year's WAIC, AI Agents became a key direction of focus in the AI industry. As large model capabilities continue to strengthen, AI is evolving from traditional question-answering assistants to intelligent agents that can understand goals, call tools, and execute tasks. This change is prompting enterprises to rethink how AI is applied.

In the past, enterprises mainly explored the AI Copilot model, using AI to assist employees in tasks such as information retrieval, content generation, and analytical summarization. As Agent technology develops, AI is further moving toward the Autopilot model, gradually participating in business process operation and complex task execution.

Manufacturing is precisely an important application scenario for this trend. Modern manufacturing systems involve multiple complex links such as production planning, equipment operation, quality control, and energy management, and behind each link lie numerous problems requiring analysis and decision-making. For example, an equipment anomaly requires comprehensive judgment based on real-time operating data, historical fault records, process parameter changes, and equipment status; a quality fluctuation requires correlating multiple variables such as equipment status, production processes, and inspection results to identify root causes. Solving these problems requires AI that understands industrial processes and can connect data, systems, and business operations to achieve a closed loop from problem detection to optimized execution.

Centering on this direction, Gtrontec continues to explore the application of the Octopus Brain industrial intelligent decision-making hub and industrial AI Agents in manufacturing scenarios. By building agent capabilities for production, quality, equipment, energy, logistics, and other fields, AI is enabled to go deeper into core manufacturing processes, driving manufacturing systems from data perception toward intelligent decision-making.

The competition of future smart factories will not only be about the number of automation equipment, but also about the collaborative capability of industrial intelligent agents.

From Intelligent Robots to Embodied Intelligence: AI Is Reshaping Industrial Hardware Forms

Embodied intelligence is an important direction continuously followed by WAIC 2026. From intelligent robots to smart equipment, the development of embodied intelligence is pushing AI to break through the boundaries of digital space, giving machines the ability to perceive environments, understand tasks, and execute actions. Behind this trend is the direction of AI developing deeper into the physical world.

Manufacturing is naturally an important application field for embodied intelligence. In the past, automation technology solved production execution problems by using robots and automated equipment to improve manufacturing efficiency and stability. However, as the industry moves toward high-end development, the problems faced by manufacturing enterprises are becoming more complex: How can equipment operating status be predicted in advance? How can process parameters be dynamically optimized? How can abnormal situations be adjusted autonomously? How can production resources be intelligently dispatched? These problems go beyond the capability boundaries of traditional automation systems and require AI to participate in analysis, judgment, and optimization decisions during the production process.

Especially in high-end manufacturing fields such as semiconductors and new energy, where production processes are complex, equipment value is high, and process requirements are stringent, the demand for intelligent capabilities is even more urgent. The future direction of manufacturing will be the deep integration of AI, industrial systems, physical AI, and embodied intelligence, enabling production systems to possess stronger autonomous operation and continuous optimization capabilities.

Gtrontec has long focused on intelligent upgrading in advanced manufacturing. At the embodied intelligence level, its mature AI+AMHS intelligent logistics solution horizontally covers all material flow scenarios in semiconductor factories, from warehouse storage and inter-line transport to machine loading and unloading. Vertically, it is supported by AI, multi-source data fusion, and Physical AI technologies, creating an end-to-end closed-loop intelligent material handling and decision-making system from "brain decision-making" to "limb execution."

At the same time, the company is actively advancing its strategic extension from physical AI to embodied intelligence. It has engaged in strategic cooperation with leading embodied intelligence enterprises, sinking AI capabilities from system software into the hardware devices themselves, so that each transport device not only executes commands but also possesses autonomous perception and local decision-making capabilities. This truly realizes "remote planning, local decision-making" and builds a more agile and efficient intelligent production system.

Industry Large Models Go Deeper into Vertical Domains: Industrial Knowledge Becomes Key to AI Implementation

Industry large models are another important direction at WAIC 2026. With the rapid development of general large model capabilities, AI is accelerating its entry into vertical industry domains.

However, manufacturing places higher demands on AI. Industrial production contains a large amount of professional knowledge, including equipment operating rules, process control logic, quality management experience, and production optimization methods. This knowledge has long been accumulated in engineering experience and business processes, and it determines whether AI can truly serve production.

Currently, manufacturing enterprises have accumulated large amounts of data resources, but the release of data value still faces challenges. Data is scattered across different systems, business knowledge sedimentation is insufficient, and production optimization still relies heavily on manual experience. The development of industrial AI requires further promoting the deep integration of large model capabilities with industrial knowledge, professional models, and business processes.

Through the collaboration of large and small models, large models handle complex understanding and reasoning planning, while industrial models handle professional analysis and accurate prediction, enabling AI to possess both general intelligent capabilities and meet industrial requirements for reliability, real-time performance, and professionalism.

Gtrontec continues to explore industrial large model applications. Through the collaboration of large and small models, it combines general AI capabilities with manufacturing expertise, allowing AI not only to analyze data but also to understand manufacturing logic, assist enterprises in making more complex production decisions, and make AI better understand industry and get closer to production needs.

After WAIC: Industrial AI Competition Enters New Stage of Industry Understanding and Value Realization

Looking back at the development of manufacturing, automation improved production efficiency, digitalization improved management capability, and AI is now driving manufacturing systems further toward intelligence. In the future, the key to competition among manufacturing enterprises will be reflected not only in production scale and equipment capability, but also in the ability to use AI to continuously optimize production systems. WAIC showcases the frontier directions of AI development, while the manufacturing site will determine the ultimate height of AI value realization.

As technologies such as AI Agents, embodied intelligence, and industry large models continue to mature, industrial AI is becoming an important bridge connecting artificial intelligence and the real economy.

Gtrontec will continue to explore industrial AI innovation, centering on the construction of the Octopus Brain industrial intelligent decision-making hub, enabling AI to go deeper into core manufacturing links, and driving enterprises from "seeing" to "acting," from automation to intelligence and autonomy.