AI shifts from being a bystander to an active participant: industrial intelligent agents showcase their capabilities in traditional industries

Securities Times Reporter Huang Xiang

“Previously at the coal washing plant, the master operator relied solely on ‘touch and feel’ to adjust the heavy medium density, taking 5-6 years to develop ‘fire-eyed golden eyes’; now, the intelligent agent directly provides the optimal parameters, and the PLC equipment automatically executes them, resulting in stable and high-quality clean coal.” At the coal washing workshop of Xinglongzhuang Coal Mine, an operator shared the real changes brought by AI intelligent agents to the traditional coal industry.

The industrial scene is highly complex, with strict safety requirements and real-time demands. The effectiveness of AI large models is limited in such environments. Against this backdrop, the industry has begun exploring and implementing AI intelligent agents.

Recently, Securities Times reporters visited Yunding Technology and found that in traditional heavy industries such as mining, chemical, oil, and gas, common issues like low efficiency, high safety risks, and heavy reliance on manual experience have long existed. However, these problems are now seeing systematic solutions—centered around the “perception—decision—execution—optimization” closed-loop capability of intelligent agents, which is reshaping industrial production and management models. As the core carrier connecting AI large models with industrial scenarios, intelligent agents are bridging the “last mile” of AI implementation, helping traditional industries transition from “point intelligence” to “system collaboration.”

Intelligent Agents Solve Industry Pain Points

“Previously, large models provided foundational capabilities, like installing a ‘smart brain’ for the industry, but intelligent agents are the ‘hands and feet’ that make the brain operational, truly turning technology into tangible benefits,” said Gao Zhen, Director of AI Business at Yunding Technology’s Industrial Internet Division, to Securities Times.

“Traditional industry digital transformation was often limited to ‘alarm-based’ applications. The gap between the capabilities of large models from ‘discovery and perception’ to ‘decision and execution’ still exists,” Gao explained. The emergence of intelligent agents has thoroughly changed this situation, showing multi-point breakthroughs in fields like mining, chemical, and oil & gas, transforming AI from a ‘bystander’ into an ‘active participant.’

Yunding Technology is a leading domestic provider of digital intelligence solutions with vertical domain large models. It has developed several typical applications in mining, chemical, and oil & gas industries, achieving large-scale promotion.

At the washing and sorting workshop of Xinglongzhuang Coal Mine in Shandong, Yunding’s intelligent agent has enabled precise density regulation in industrial scenarios. Traditional heavy medium separation relies on manual experience to set densities, leading to large parameter fluctuations, unstable clean coal yield, and waste of medium and coal losses. Now, the intelligent agent predicts the optimal separation density using a large predictive model, directly drives the PLC to perform closed-loop adjustments, stabilizing coal quality and increasing yield by over 0.2%. Based on an annual washing capacity of 3 million tons, this can generate direct economic benefits exceeding 3 million yuan each year.

Safety in underground operations has also been transformed by intelligent agents. At the Li Lou Coal Mine’s blast and pressure relief drilling site, the anti-blast pressure relief borehole depth monitoring intelligent agent automatically counts drill rods via video algorithms, eliminating the old manual ‘one-by-one’ verification method prone to errors.

“Before, counting drill rods manually was eye-straining and prone to missing counts. Now, with algorithms for automatic verification, work efficiency has increased by over 80%,” said a现场 worker. The intelligent agent also takes over conveyor belt inspections, with 24-hour real-time video monitoring, automatic alerts for anomalies, and coordinated responses, reducing workers’ labor intensity and eliminating blind spots in manual inspections.

In the chemical industry, intelligent agents aim to tackle the complex, nonlinear, and strongly coupled process optimization challenges. “Coal washing mainly involves physical changes, while chemical processes involve chemical reactions. Adjusting one parameter can trigger chain reactions, making prediction and optimization significantly more difficult,” Gao said. The development of intelligent agents for methanol distillation, for example, took nearly a year of dedicated effort by the company’s AI team. The system’s deployment at Yulin Petrochemical resulted in a 3.2% reduction in methanol vapor consumption, an annual increase of 180 tons in methanol production, and cost reductions and efficiency gains of over 4.5 million yuan per plant per year.

The oil and gas sector also demonstrates scalable deployment of intelligent agents. In 2024, Yunding secured a project with a pipeline network group to extend intelligent agent capabilities into oil and gas pipeline management. “From mining to chemical to oil & gas, the rapid promotion of intelligent agents is mainly because they address real industry pain points and deliver visible benefits,” Gao said.

Building ‘Hard Support’ for Traditional Industries

Behind the successful application of intelligent agents in traditional industries is a technical system tailored to industrial scenarios. Unlike the general-purpose intelligent agents for consumer applications, industrial intelligent agents focus more on “practicality” and “safety,” forming a core architecture of “multi-modal base + data fuel + platform carrier.”

As early as 2022, Yunding partnered with Huawei to develop large models. In 2023, it launched the first mining large model for the energy industry, followed by the Fuxi chemical large model in 2025. Today, a family of industrial large models covering multiple sectors has been established. “Our large model base is multi-modal driven, including local deployment of commercial models like Huawei Pangu, as well as integration of mainstream general models, allowing flexible adaptation to different scenarios,” Gao explained. This “industry + general” design enhances technological resilience.

“Industrial intelligent agents cannot rely solely on general data; they must be rooted in industry-specific data,” Gao revealed. Since the initial development of industry large models, Yunding has focused on accumulating industry data, now possessing over one million annotated industry data points and hundreds of billions of production data entries. Its industry data set has been included in the 2025 national high-quality data set pilot project. These data, infused with industry-specific insights, make intelligent agent decisions more accurate and practical.

Yunding’s self-developed Cangjie intelligent agent platform simplifies deployment. “We want frontline workers who don’t know programming to also use intelligent agents,” Gao said. The platform features application orchestration and multi-agent collaboration, allowing users to drag and drop components to quickly build custom intelligent applications. Currently, it supports natural language processing scenarios, with plans to expand into industrial safety monitoring, process optimization, and other complex applications.

A key requirement for industrial intelligent agents is embedded “safety genes.” Given the zero-tolerance safety standards in industrial environments, these agents must incorporate comprehensive safety mechanisms during design and operation. For example, full-chain audit logs of operations, automatic shutdown in case of anomalies, and strict safety checks for industrial skill packages.

“OpenClaw’s popularity confirms the value of deploying intelligent agents, but compared to general capabilities, we focus more on standardizing and encapsulating years of industrial algorithms and experience into reusable ‘industrial skill packages,’ which is our core advantage,” Gao emphasized.

Accelerating Through Challenges

While the application of intelligent agents in traditional industries is progressing, several practical challenges remain.

“Industrial scenarios are complex and open, with significant differences in processes and equipment, making it difficult for general-purpose intelligent agents to be effectively deployed,” Gao noted. For example, in temporary support during coal mining, some mines use airborne temporary supports, others use single units, requiring different monitoring solutions. Additionally, challenges such as difficulty in retrofitting old plants, data silos, and lack of standardization hinder large-scale industry adoption.

More importantly, there are notable differences between industrial intelligent agents and consumer-oriented ones. “Consumer agents emphasize generality, with skill packages that are highly reusable; industrial agents, however, focus on deep integration with specific scenarios, often requiring customized interfaces and capabilities for different equipment and processes,” Gao said. Although industrial intelligent agents are less mature than consumer ones, this is also their strength—“solving the tough problems in complex scenarios.”

“Due to the complexity, specificity, and openness of industrial environments, current intelligent agents are mostly applied to individual production steps or localized scenarios. The next step is to develop multi-agent collaboration to integrate scattered point scenarios, creating ‘agent groups’ that form systematic solutions such as emergency management, safety scheduling, and risk warning systems, ultimately aiming to build a true ‘AI brain,’” Gao envisioned.

Yunding’s mining large model has been recognized as internationally leading by the China Coal Industry Association, with capabilities evaluated by authoritative domestic third-party institutions to be among the top tier globally. To date, its 223 AI scenarios have been implemented in over 130 production units including China Coal, State Pipeline Network, and Wanbei Coal & Electricity.

“Our strength isn’t in the number of parameters but in solid scenario implementation,” Gao said. Yunding is committed to managing visual, predictive, and natural language processing intelligent agents centrally, beyond just single applications.

Policy support is also strong. The National Energy Administration and other departments have issued policies encouraging deep integration of AI with the energy industry, providing robust support for intelligent agent applications. With tangible results, intelligent agents are helping traditional industries shift from “experience-driven” to “data-driven” approaches.