AI agents enable companies to make smarter, faster, and more informed decisions. From predictive maintenance to real-time process optimization, these agents are delivering tangible benefits across industries.
In today’s rapidly evolving industrial landscape, data has become the lifeblood of operational efficiency, innovation, and competitive advantage. However, the sheer volume and complexity of industrial data often overwhelm traditional methods of analysis and decision-making. Increasingly, companies in industrial sectors are turning to AI agents, which are software programs that use artificial intelligence to autonomously perform focused tasks, learn from data, and interact with their environment without continuous human intervention. Such agents have the potential to transform the way companies operate, driving efficiencies, improving safety, and unlocking new business opportunities.
Most importantly, AI agents can bring advanced capabilities, including real-time data analysis, predictive modeling, and autonomous decision-making, available to a much wider group of people in any organization. That, in turn, gives companies a way to harness the full potential of their data. Simply put, AI agents are rapidly becoming essential tools for business managers and data analysts in industrial businesses, including those in chemical production, manufacturing, energy sectors, and more.
See also: Using Generative AI to Improve Industrial Workflows
AI Agents in Action Across Industries
AI agents are nothing new. For years, they have been available to automate tasks. However, they had limited functionality. They were usually designed to work with a limited set of data and inputs, and they had to be programmed to perform specific tasks.
We are now in a new era of AI agents. They are more intelligent and use reasoning. That makes them more adaptable to changes and expands their role beyond the execution of a particular task.
Take the example of a chatbot. In the past, a chatbot would have been programmed, and perhaps some machine learning capabilities could have been used to get better at certain answers. However, it did not offer the reasoning capabilities that can be endowed using a Large Language Model.
As such, the new generation of AI agents incorporates reasoning capabilities that are closer to the human experience both on the input side and the user experience side of things. That allows them to take on more complex tasks.
Chemical Industry AI Agents
In the chemical industry, AI agents can monitor and control chemical processes in real time, minimizing risks associated with equipment failures, leaks, or hazardous reactions. By analyzing data from sensors and operational equipment, AI agents can predict potential failures and recommend preventive maintenance actions. This reduces downtime, improves safety, and enhances overall production efficiency. Furthermore, AI agents help optimize the usage of raw materials, reducing waste and promoting more sustainable practices.
For example, an AI agent can be used to monitor temperatures, pressures, and chemical compositions in reactors, ensuring that all conditions remain within optimal parameters. If anomalies are detected, the AI agent can alert human operators or even take corrective action autonomously to prevent accidents.
Manufacturing AI Agents
The manufacturing sector is heavily reliant on complex machinery, which requires regular maintenance and precise calibration. AI agents can help facilitate the use of predictive maintenance by constantly monitoring equipment health and predicting when failures are likely to occur. By analyzing machine data, AI agents can determine the optimal times for maintenance, reducing downtime and extending the lifespan of equipment.
AI agents might also be used to manage robotics in assembly lines. These agents can coordinate different machines, improving the flow of production, reducing energy consumption, and increasing output while maintaining high-quality standards.
In addition to aiding operational aspects of the manufacturing process, AI agents can help manufacturers optimize their supply chains. From inventory management to demand forecasting, AI agents ensure that the right materials are available at the right time, minimizing bottlenecks and preventing production delays.
Energy AI Agents
Energy sector companies can leverage AI agents to improve grid management, optimize energy distribution, and enhance asset performance. AI agents can also be used to monitor energy consumption patterns, predict demand spikes, and ensure the efficient distribution of energy across networks.
In the renewable energy space, AI agents can play a critical role in maximizing the efficiency of solar panels and wind turbines. AI agents can make adjustments to optimize energy capture and distribution by continuously analyzing environmental data and operational metrics. For example, an AI agent might be used to predict weather patterns and adjust the positioning of solar panels to capture the maximum amount of sunlight.
Additionally, in oil and gas, AI agents can help monitor the integrity of pipelines and drilling equipment, detecting early signs of wear and tear. This proactive monitoring prevents costly equipment failures and environmental hazards.
See also: How and Where to Start with AI for Industry
Emerging Categories of AI Agents
New categories of function-specific AI agents are emerging as organizations become more familiar with the technology. Such agents address particular challenges that are common across industries. Some examples include:
Data Insight Agents
These AI agents analyze vast amounts of data to generate actionable insights. By processing historical and real-time data, these agents can uncover hidden patterns, trends, and correlations that would be difficult for humans to detect manually. For example, in manufacturing, a data insight agent might analyze production data to identify inefficiencies or anomalies, enabling managers to make informed decisions that improve operational efficiency.
Use Case Agents
Use case agents are designed to handle specific tasks or processes within a business. These agents can be deployed for tasks such as quality control, resource allocation, or workflow management. In the chemical industry, a use case agent might be used to monitor the quality of raw materials in real time, ensuring that they meet the required specifications before entering the production process. In doing so, these agents help maintain product quality while minimizing waste.
Operational Agents
Operational AI agents focus on improving the day-to-day operations of industrial businesses. These agents can automate routine tasks, such as scheduling maintenance activities, managing logistics, or optimizing production processes. For instance, in the energy sector, operational agents might be used to autonomously manage grid loads, ensuring that energy is distributed efficiently based on demand.
Working With a Technology Partner
Organizations can certainly develop their own AI agents using available technology. However, many organizations do not have the in-house skills or time to bring the needed elements together to develop functional AI agents.
Increasingly, many organizations are turning to a technology partner that brings expertise in both AI technology and the application of that technology in various industries. Those are areas where Cognite can help.
Cognite offers a suite of data and AI capabilities specifically designed to address the unique challenges faced by industrial companies and help them build their portfolio of agents. Cognite’s AI agents can help accelerate root cause analysis, assess equipment anomalies, and help improve data discovery and analysis across a wide range of industries.
For AI agents to be successful and not return hallucinations, they need to be trained on contextualized data. Cognite can help in two main ways. They are: 1) developing industrial agents using the Cognite Atlas AI workbench and 2) curating and contextualizing the data to train the agents for best outcomes using Cognite Data Fusion.
A Final Word in Industrial AI Agents
AI agents enable companies to make smarter, faster, and more informed decisions. From predictive maintenance to real-time process optimization, these agents are delivering tangible benefits across industries.
For business managers and data analysts, the key takeaway is clear: AI agents are not just a future possibility—they are a present necessity, capable of driving efficiency, innovation, and growth in today’s competitive industrial environment.
