Workshop “Creating an AI Agent for the Oil and Gas Industry”
On October 23, 2026, Gubkin University hosted a workshop, “Creating an AI Agent for the Oil and Gas Industry”, organized by the Student Scientific Society (SSS) of Gubkin University.
The event was led by Vitaly Pshenichny, Acting Dean of the Faculty of International Energy Business, Associate Professor of the Department of Strategic Management of the Fuel and Energy Complex, and Candidate of Economic Sciences.
The workshop was attended by 58 students. Participants learned about the principles of how AI agents work and the main stages of creating them – from setting the task and preparing source documents to developing instructions, testing, and verifying the result.
Special attention was paid to the potential uses of AI assistants for working with documents and information in the oil and gas industry. Participants discussed which tasks could be partially automated with such tools and what role human expert review continues to play.
In the practical part, Vitaly Pshenichny demonstrated the setup of a training AI assistant for preparing equipment malfunction reports. Using the example of a message about unusual noise from a pump, the students followed how the agent consults regulations and the equipment register, identifies missing information, requests clarification, and drafts a preliminary version of the report.
Particular attention was given to the principle of user control. In the training scenario under review, an entry was made in the log only after human confirmation, and the AI agent was not designed with functions for directly controlling equipment.
Participants also discussed the need to assess the economic effect of introducing AI agents and the balance between the potential benefits of automation and the costs of developing, implementing, and subsequently maintaining the digital solution.
The workshop gave students an introduction not only to the technological principles of creating AI agents, but also to practical approaches to their safe and well-grounded use in solving applied problems in the oil and gas industry.
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