Generative vs. agentic AI in the energy industry’s path to autonomy

The adoption of artificial intelligence (AI) in oil and gas and energy has rapidly progressed, evolving from traditional AI applications focused on prediction, classification, and optimization to generative AI tools that improve access to technical knowledge and accelerate information synthesis. Today, the industry is increasingly looking toward agentic AI, where systems can reason across multiple data sources, plan sequences of work, interact with enterprise tools, and support defined workflows within governed boundaries. While this shift marks an important step toward more autonomous operations, it also introduces new risks. Successful implementation requires a methodical approach that identifies high-value use cases, builds trust through measurable performance, and expands autonomy only where reliability has been proven.