Agentic AI Use Cases for Today’s Natural Resources and Energy Firms

The natural resources and energy industry is on the verge of a significant transformation, fueled by rapid advances in agentic artificial intelligence (AI) capabilities. Unlike conventional automation, agentic AI systems are purpose-built to execute complex, high-value tasks with little human intervention. Capable of learning, adapting, and making real-time decisions, intelligent agents are poised to bring improvements and efficiencies across the value chain, from seismic exploration and drilling optimization to predictive maintenance and supply chain management. 

Early adopters are already realizing tangible benefits, including enhanced safety protocols through real-time hazard detection, accelerated incident response times, more efficient asset utilization, and more accurate project forecasting. By leveraging agentic AI to its full potential, natural resources and energy companies can reduce downtime, lower operational costs, and improve their compliance programs. 

But the path to agentic AI integration is not without obstacles. Intelligent agents require robust support infrastructure, without which they can expose companies to costly reputational or enforcement risks. Firms will need stronger data governance processes and enhanced cybersecurity protections to mitigate threats and improve outputs. 

For natural resources and energy companies, proactivity will be the key to long-term agentic AI success. Firms should explore high-impact use cases and build a strategic roadmap for AI integration. Those who embrace agentic AI now, and upgrade their systems and processes, accordingly, can position themselves to future proof their organizations and secure a significant competitive advantage. Those who delay risk falling behind their peers in a rapidly evolving market. 

Industry Use Cases for Agentic AI

AI agents have a wide range of promising applications across the natural resources and energy value chain. These use cases touch upstream, midstream, and downstream operations, and carry the potential to boost efficiency, enhance safety, reduce operational overhead, and contribute to a more agile and resilient organization. 

Upstream (Exploration & Production) 

Exploration and Drilling Optimization

AI agents can analyze vast and complex datasets, such as seismic readings and geological surveys, to pinpoint potential drilling or mining sites with high accuracy. Automating these evaluations can save additional time and resources and insulate them against costly human errors. Once suitable sites have been identified, intelligent agents can also adjust drilling paths in real time based on changing geological conditions and equipment performance. 

Predictive Maintenance

Autonomous agents can track equipment health in drilling rigs and other upstream assets by monitoring sensor data — tracking factors like vibrations and temperature, for example — to predict failures before they occur. When an agent detects that an equipment fault is likely, it can automatically flag the problem to management, schedule maintenance, order necessary parts from contracted suppliers, and inform on-site crews.  

Enhanced Reservoir Management

By integrating seismic data, well logs, and production history in real time, intelligent agents can make dynamic adjustments to improve reservoir performance. For instance, a company could give an AI agent control over a parameter like injection rates, allowing it to monitor water usage and conserve resources where possible, while still maintaining an optimal output. 

Midstream (Transportation & Storage) 

Pipeline Integrity and Leak Detection

With access to data from sensors, drones, and satellite imagery, agentic AI can detect anomalies in pipeline function and identify potential leaks. If a leak is flagged, and subsequently verified by a human operator, the system can then autonomously dispatch a repair crew to the area, giving them time to address a nascent issue before it escalates — improving both pipeline safety and overall performance. 

Logistics and Route Optimization

Shifting market conditions and production requirements often demand changes to transportation routes and logistics for natural resources and energy products. Intelligent agents can monitor relevant variables and quickly adjust shipping schedules, enabling companies to scale up and accelerate when the need arises.  

Predictive Maintenance

Like its upstream application, AI agents enable firms to predict maintenance needs for transportation infrastructure. If, for example, a pump or compressor fails, the ensuing disruption can halt the flow of crucial resources, harming both the company itself and those who depend on it. By identifying and flagging potential problems early, intelligent agents allow crews to keep equipment running smoothly, saving both time and money that might otherwise have been spent addressing a larger malfunction later. 

Downstream (Refining & Marketing) 

Adaptive Refining Control

Over- or underproduction can be costly, and AI agents can continuously adjust refining operations to match yields with current market demands and up-to-date operational data. They can even check to ensure that refining parameters are aligned with product specifications to reduce emissions and improve sustainability. 

Fuel Demand Forecasting

In addition to short-term, reactive measures, intelligent agents can also support long-term planning. By analyzing historical market data, AI agents forecast future retail fuel demand, allowing companies to create detailed production and distribution plans. 

Automated Compliance and Reporting

From environmental data and tax filings to safety reports and labor practices, natural resources and energy companies are responsible for tracking and transmitting large amounts of compliance data. Even with the aid of more basic automation tools, collecting and sorting this information can be costly. AI agents can help streamline these processes, tracking regulatory requirements across jurisdictions and formatting reports to align with rulemaking standards and stakeholder expectations. 

Building a Pipeline to Agentic AI Success

Agentic AI systems can bring numerous benefits across their operations, but natural resources and energy companies cannot view AI implementation as a passive endeavor. To get the most out of their AI investments, companies will need to institute a strong governance infrastructure and improve their cybersecurity programs to counter new threats surfaced by agentic AI use. 

Governance

A strong and well-defined AI governance program is crucial for a successful rollout. As illustrated above, autonomous agents will touch a wide array of functions, and companies should approach building a governance framework as a cross-functional process that solicits input from across the organization and addresses industry- and business-specific risks. 

Natural resources and energy companies should place a special emphasis on decision verification. For example, an AI agent in an upstream role might identify a need to halt operations at a particular site to perform equipment maintenance, but this decision should first be vetted and verified by human operators. If the agent is mistaken, or its conclusion is derived from a faulty or incomplete data set, then a stoppage could cause the company to incur costs it might otherwise have avoided. On the other hand, agentic AI should never fully replace human equipment inspections. Rather, companies should assess the accuracy of their AI agents and set a new inspection cadence that captures cost and time savings, but still adheres to legal standards and best practices. Used responsibly, intelligent agents can enhance safety measures and protocols but should not be left entirely in charge of administering them. 

In setting guidelines for human oversight and verification, companies should clearly define where agents will be permitted to make autonomous decisions, what data they will draw from, and how outputs will be audited. All oversight decisions and resulting feedback, particularly in areas with implications for worker safety, should be documented for future reference and iteration. 

Cybersecurity

Natural resources and energy companies are no strangers to cybersecurity concerns, as they are frequent targets for disruption. Interconnected AI systems can further increase their security vulnerabilities, calling for even more robust detection and prevention measures. 

With respect to agentic AI, insider risk can pose a particular threat. As more employees interact with AI agents, the chances grow that someone will inadvertently expose sensitive data via an AI prompt or query. To guard against insider risk, companies should closely monitor all agent inputs — just as they verify agent outputs — to ensure they remain within scope and do not divulge (or ask the agent to divulge) privileged information. 

On work sites, physical security is also important. Certain employees may be granted access to smart devices that communicate with AI agents, and these devices must not leave the premises or be given to any individuals who are not designated users. Most cyber events stemming from insider actions are accidental, not malicious, and effective employee training and permissions monitoring can go a long way to preventing a breach.