Is the Energy Industry Ready for Agentic AI?
The energy industry, often characterized by its conservative approach and high-stakes environment, stands at a pivotal point: is it ready to transition from incremental innovation to the transformative potential of Agentic AI? While generative AI (GenAI) has delivered real value across industries, Agentic AI represents a more advanced paradigm. Unlike GenAI, which focuses on generating content or automating simple workflows, Agentic AI is designed to take autonomous actions to achieve specific goals that drive measurable outcomes. This capability introduces both opportunities and challenges, particularly in a risk-averse sector like energy.
In EPAM’s AI Research Report, From Hype to Impact: How Enterprises Can Unlock Real Business Value with AI, we found that over 65% of our energy industry respondents indicate they have already deployed GenAI to deliver impactful capabilities such as rapid interactions with technical document, data and image libraries; prescriptive recommendations for operational performance insights; and real-time support for field engineers, vendors and customers. Yet, few have operationalized Agentic AI, which represents the “last mile” of AI adoption: where intelligent agents not only collaborate with humans but autonomously optimize operations at scale.
In this blog, we’ll explore the next iteration of AI that we see on the horizon: Agentic AI. We’ll talk about what Agentic AI is, how it differs from GenAI, its potential applications in the energy industry and the barriers that may hinder its adoption.
What is Agentic AI?
To understand Agentic AI, it’s important to distinguish it from related concepts like Agentic workflows. While Agentic workflows involve AI systems assisting in decision-making processes by suggesting the next steps in a sequence, Agentic AI takes it a step further by autonomously executing actions based on predefined goals.
David Aldrich explains that Agentic AI means you have an AI system that can autonomously take action to achieve specific goals. It doesn’t just analyze or generate content—it executes without intervention. This autonomy is what sets Agentic AI apart.
Why is the Energy Industry Hesitant?
The energy industry has yet to fully embrace Agentic AI, and for good reason. Sergey Sergeev points out that the energy industry is a conservative industry with a lot at stake. While the vision is compelling, the energy sector’s risk profile, regulatory landscape and culture present unique barriers to large-scale agentic adoption.
The sector’s risk-averse nature stems from several factors:
Human Safety: In high-stakes environments like oil and gas, where human lives and environmental safety are on the line, the idea of delegating control to an autonomous system can be unsettling. For instance, allowing AI to autonomously adjust drilling parameters or dispatch field service teams introduces risks that many companies are not yet willing to accept.
Regulatory Constraints: Strict regulations often limit the degree of autonomy that can be granted to AI systems in critical energy operations. Oversight and accountability remain non-negotiable.
Uncertain ROI: Even when the technology exists, the return on investment (ROI) for implementing Agentic AI can be unclear. Sergey notes that even if something can be done, it might not get done because of the uncertain ROI or safety and security risks.
Internal Resistance: Beyond external factors, internal resistance from subject matter experts (SMEs) and employees adds another layer of complexity. Concerns about job security and skepticism about AI capabilities can lead to reluctance to adopt new technologies.
Agentic AI in Energy: Current Applications and Future Potential
While full-scale adoption of Agentic AI may still be years away, the energy industry is already leveraging aspects of Agentic workflows, representing “Agentic AI baby steps” for this conservative, risk-averse industry. These workflows involve structured, AI-assisted tasks where humans remain in the loop, which helps address some of the hesitancy issues around human safety, regulatory constraints, and internal resistance. For example:
Drilling Optimization:
AI systems can monitor drilling parameters like RPM and mud properties, identify patterns that indicate potential issues (e.g., stuck pipe incidents) and recommend adjustments to optimize the rate of penetration. However, the final decision to implement these recommendations typically rests with a human engineer.
Production Optimization:
AI agents can monitor production rates across multiple wells, analyze fluid composition and suggest adjustments to artificial lift systems to balance production.
These recommendations are presented to engineers, who then decide on the appropriate actions.
Pipeline Integrity:
AI workflows can analyze real-time flow, pressure and temperature data to detect anomalies and recommend actions like adjusting flow rates or isolating sections of a pipeline. Again, human oversight ensures that these actions align with safety protocols and operational goals.
Maintenance Planning:
In maintenance operations, AI can help generate crew schedules, order necessary parts and prepare safety plans. However, the execution of these plans still requires human intervention.
Looking ahead, the vision for Agentic AI in energy could include systems that autonomously prepare platforms for approaching storms, optimize production across assets for maximum net present value, or even execute full maintenance turnarounds. For the future vision of Agentic AI, David talks about imagining an operations manager asking AI to model a full maintenance turnaround on a platform. The AI could coordinate production adjustments, generate crew schedules, order parts and prepare safety permits — all through a network of specialized agents.
Single-Agent vs. Multi-Agent Systems
One of the key design principles in Agentic AI is deciding when to use a single agent versus multiple specialized agents. A single agent might handle tightly coupled operations, such as all aspects of a drilling operation. However, for complex, multi-domain problems — like coordinating drilling, production and maintenance across multiple facilities — multiple specialized agents are more effective.
David explains that each agent should have access to the minimum data it needs to achieve its specific goal. This prevents information overload and reduces latency in response times. For example, a drilling agent focuses on drilling parameters and mud logs. A production agent monitors flow rates, pressure and temperature. A maintenance agent handles crew scheduling and equipment needs. These agents can collaborate within a larger system, orchestrated by a central AI, to achieve complex goals while maintaining efficiency and clarity.
Is the Energy Industry Ready?
While the energy industry may not yet fully be prepared to embrace the transformative potential of Agentic AI, the time to start considering its integration is now. The sector’s conservative nature, coupled with high-stakes operations and regulatory constraints, understandably slows adoption. However, the mounting pressures of rising labor costs, aging infrastructure, and intensifying competition demand innovative solutions to drive operational efficiencies and maintain a competitive edge.
Agentic AI, with its ability to autonomously optimize operations at scale, represents a promising pathway to achieving these goals. By beginning with narrow, well-defined use cases and gradually scaling capabilities, the energy industry can mitigate risks and build confidence in this advanced technology. As the landscape evolves, those who proactively explore Agentic AI will be better positioned to lead the charge in reshaping energy operations for a more efficient, safe, resilient, and sustainable future.
