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

A certain coal mine in Shandong implements intelligent underground operations. Provided by the interviewed company / supplied image

Securities Times Reporter Huang Xiang

“Previously at the coal washing plant, experienced workers relied solely on ‘touch and feel’ to adjust the heavy medium density, taking 5-6 years to develop ‘fire-eyed’ intuition; now, the intelligent system directly provides optimal parameters, and 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 systems to the traditional coal industry.

Industrial scenarios are highly complex, safety requirements are strict, and real-time performance is critical. 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 are being systematically addressed—through the development of a closed-loop capability of “perception—decision—execution—optimization” centered on 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’ in 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.

“Digital transformation in traditional industries 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 deployment.

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 system predicts the optimal separation density using a large model, directly drives PLC equipment for closed-loop adjustments, stabilizes coal quality, and increases clean coal yield by over 0.2%. Based on processing 3 million tons annually, 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 anti-blast pressure relief drilling site, an anti-blast pressure relief borehole depth monitoring intelligent agent automatically counts drill rods via video algorithms, eliminating the old manual, tedious, and error-prone process.

“Before, counting drill rods manually was eye-straining and prone to missing counts. Now, with algorithms, verification efficiency has increased by over 80%,” said on-site staff. The coal conveyor belt inspection underground is also managed by an intelligent agent, with cameras providing 24-hour real-time monitoring, automatic alerts for anomalies, and coordinated responses—reducing workers’ labor intensity and eliminating blind spots in manual inspections.

In the chemical industry, the challenge is to optimize chemical production processes characterized by “multi-variable, nonlinear, strongly coupled” dynamics. “Coal washing mainly involves physical changes, while chemical processes involve reactions; adjusting one parameter can trigger chain reactions, making prediction and optimization significantly more difficult,” Gao said. The AI team invested nearly a year developing an intelligent agent for methanol distillation. The system’s deployment at Yulin Petrochemical resulted in a 3.2% reduction in methanol vapor consumption, an additional 180 tons of methanol produced annually, and cost reductions and efficiency gains of over 4.5 million yuan per plant per year.

In the oil and gas sector, intelligent agents are also demonstrating scalable deployment. In 2024, Yunding Technology won a project with a pipeline network group to extend intelligent agent capabilities into oil and gas pipeline networks. “From mining to chemical to oil & gas, the rapid adoption 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 in 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 Technology partnered with Huawei to develop large models. In 2023, it launched the energy industry’s first mining large model, and by 2025, it plans to release the Yunding Fuxi chemical large model. 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-specific large models, Yunding has focused on accumulating industry data, now possessing over one million labeled industry datasets and hundreds of billions of production data points. Its industry data collection has been included in the 2025 national high-quality data set pilot project. These data, imbued with “industry warmth,” enable more accurate and practical decision-making.

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 customized intelligent applications. Currently, it supports natural language processing scenarios, with plans to expand into industrial safety monitoring, process optimization, and other complex scenarios.

Most importantly, industrial intelligent agents are designed with a “safety gene.” Given the zero-tolerance safety standards in industrial environments, these agents embed comprehensive safety mechanisms—such as full-chain operation log auditing, automatic shutdown upon detecting anomalies, and strict safety checks for industrial skill packages.

“OpenClaw’s success demonstrates 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 deepening, there are still practical challenges.

“Industrial scenarios are complex and open, with significant differences in processes and equipment, making it difficult for general-purpose intelligent agents to be effectively implemented,” 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, issues like difficulty in retrofitting old plants, data silos, and lack of standardization also hinder large-scale industry adoption.

More critically, there are notable differences between consumer and industrial intelligent agents. “Consumer agents emphasize generality, with skill packages 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 like 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 domestic authoritative third-party agencies to be among the top tier globally. To date, 223 AI scenarios have been implemented in over 130 production units including China National Coal Group, State Pipeline Network Group, and Wanbei Coal & Electricity.

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

At the policy level, departments like the National Energy Administration have issued multiple policies encouraging deep integration of AI with the energy industry, providing strong support for intelligent agent applications. With tangible results, intelligent agents are helping traditional industries shift from “experience-driven” to “data-driven” operations.