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, experienced workers relied solely on ‘touch’ to adjust the heavy medium density. It took 5-6 years to develop a ‘sharp eye’; now, the intelligent system directly provides the optimal parameters, and PLC equipment automatically executes them, resulting in stable and high-quality clean coal.” At Xinglongzhuang Coal Mine’s coal washing workshop, an operator shared the real changes brought by AI intelligent systems to traditional coal industry.

The industrial scene is highly complex, safety requirements are strict, and real-time performance is critical. The effectiveness of AI large models is limited in this context, prompting the industry to explore and implement AI intelligent systems.

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 core capability of “perception—decision—execution—optimization” closed-loop systems centered on intelligent agents, which are reshaping industrial production and management models. As the key link 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.

“Traditional industry digital transformation was mostly limited to ‘alarm-based’ applications. The gap in extending 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 mining, chemical, oil, and gas sectors, transforming AI from a ‘bystander’ into an ‘active participant.’

Yunding Technology is a leading domestic provider of digital intelligence solutions with vertical large models tailored for specific industries. They have developed several typical applications in mining, chemical, and oil & gas industries, achieving large-scale deployment.

At the coal washing plant in Xinglongzhuang Mine, Yunding’s intelligent agent has enabled precise density regulation in industrial scenarios. Traditional heavy medium separation relied on manual experience to set densities, leading to large parameter fluctuations, unstable clean coal yield, and medium waste. 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 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 revolutionized by intelligent agents. At Liliu Coal Mine, the anti-blast pressure relief drilling site uses video algorithms to automatically count drill rods, eliminating the old manual, error-prone process.

“Before, counting drill rods manually was eye-straining and prone to missed counts. Now, with algorithms, verification is automatic, improving efficiency by over 80%,” said on-site staff. The coal conveyor belt inspection is also managed by intelligent agents, with 24/7 real-time video monitoring, automatic alerts for anomalies, and coordinated responses—reducing worker labor and eliminating blind spots in manual inspections.

In the chemical industry, intelligent agents tackle the complex problem of optimizing 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 challenging,” Gao said. The AI team spent nearly a year developing an intelligent system for methanol distillation. The effort paid off: after deployment at Yulin Petrochemical, steam consumption per ton of methanol decreased by 3.2%, with an annual increase of 180 tons of methanol production, and cost savings and efficiency gains of 4.5 million yuan per plant per year.

In the oil and gas sector, intelligent agents are also demonstrating scalable deployment. 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 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 success of intelligent agents in traditional industries is a technical system tailored to industrial scenarios. Unlike the general-purpose AI 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, launching the first mining large model for the energy industry in 2023, and releasing the Yunding Fuxi chemical large model in 2025. Today, they have built a family of industrial large models covering multiple sectors. “Our large model base is multi-modal driven, locally deploying commercial models like Huawei Pangu, and integrating mainstream general models to adapt flexibly 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. Yunding has prioritized industry data accumulation from the start, now possessing over one million labeled industry data samples and hundreds of billions of production data points. Their industry datasets have been included in the 2025 national high-quality data project. These data, imbued with “industry warmth,” make intelligent agent decisions more accurate and practical.

Yunding’s proprietary Cangjie intelligent agent platform simplifies deployment. “We want frontline workers who don’t know programming to also use intelligent agents,” Gao said. The platform supports 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 feature is that 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 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, 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, old plant upgrades, data silos, and lack of standardization hinder large-scale industry adoption.

More importantly, there is a significant capability gap between consumer AI agents and industrial ones. “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, integrating scattered point scenarios into ‘agent groups’ to create systematic solutions like emergency management, safety scheduling, and risk warning systems, ultimately building 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 agencies placing it in the top tier globally. To date, 223 AI scenarios have been implemented across over 130 units including China Coal, State Pipeline Network, and Wanbei Coal & Electricity.

“Our strength isn’t in the number of parameters but in solid scenario deployment,” Gao said. Yunding aims to manage various types of intelligent agents—vision, prediction, natural language processing—within a unified platform.

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 backing for intelligent agent applications. These systems are visibly driving traditional industries from “experience-driven” to “data-driven” transformation.