Agentic AI for Oil & Gas Operations
Design supervised AI agents that help oil and gas teams find approved technical information, prepare maintenance work and review production reports. This training course develops the ability to define what an agent can access and do, where engineers must approve its work and how to test its reliability.
Prepare yourself for the Agentic AI for Oil & Gas Operations course
Assess where AI agents can support production and maintenance, and define the decisions that require an authorised person’s judgement. This Agentic AI for Oil & Gas Operations training course focuses on practical tasks such as retrieving approved engineering instructions, preparing draft maintenance requests and summarising production exceptions. Participants learn to assess each task against the quality of its records, the potential value to the operations team and the consequences of an error.
Participants then map how the agent would use approved information and tools, where its work would be checked and when it should stop or escalate an exception. Using representative petroleum scenarios, they examine how outdated records, ambiguous instructions and incorrect tool actions can affect the reliability of a workflow. They apply these findings to a supervised agent blueprint that defines the workflow boundaries, approval points and evidence needed for operational review.
Highlights of this Agentic AI for Oil & Gas Operations training course includes:
- Selection of AI-agent use cases for production reporting, maintenance preparation and operational handovers
- Retrieval of approved technical documents with traceable references
- Definition of tool permissions, engineer approval points and escalation routes
- Testing for unreliable responses, incorrect tool actions and operational exceptions
- Assessment of workflow readiness using source quality, integration requirements and performance criteria
The learning journey
Course Summary Outline
Day 1
Agentic AI Opportunities in Oil & Gas
Day 2
Designing Supervised Operational Agent Workflows
Day 3
AI Agents for Production and Maintenance Support
Day 4
Operational Safety, Security and AI Assurance
Day 5
Operational Readiness and AI-Agent Performance Evaluation
Is This Course Right for You?
The Agentic AI for Oil & Gas Operations training course is designed for professionals responsible for designing, reviewing or improving AI-supported production and maintenance workflows.
- Oil and Gas Digital Transformation Managers
- Industrial AI Product Owners
- Operations Improvement Managers
- Production Engineers
- Maintenance Engineers
- Maintenance and Reliability Managers
- Industrial Data Managers
- IT/OT Integration Leads
- Technical Assurance Leads
Course Summary
- AI agents, conventional AI, RPA and industrial controls
- Upstream, processing and maintenance use cases
- Task boundaries, handovers and escalation points
- Agent instructions and traceable retrieval from approved documents
- Production surveillance and exception reporting
- Work-order triage, job preparation and engineering instructions
- AI-agent boundaries for OT, DCS and SIS
- Data classification, least-privilege access and third-party connections
- Source quality, integration feasibility and supplier requirements
- Workflow ownership, engineering review and approval criteria
Want the full outline?
Key Learning Outcomes and Objectives?
The Agentic AI for Oil & Gas Operations training course equips participants to assess whether an operational support task is suitable for a supervised agent and turn that assessment into a workflow design. Participants learn to specify approved sources, tool permissions and engineering review points, then develop tests and measures that help production, maintenance and assurance teams judge how reliably the proposed workflow handles routine tasks and exceptions.
- Prioritise production and maintenance support tasks by value, data readiness and operational risk.
- Design agent instructions and retrieval steps that trace information back to approved technical sources.
- Define tool permissions, handovers, engineer approvals and escalation conditions.
- Identify risks from unreliable retrieval, prompt injection and incorrect tool actions.
- Set test scenarios and performance criteria for accuracy, task completion, exceptions and operator effort.
- Present a supervised agent blueprint and explain the evidence and limitations that affect its readiness.
The Excentra Learning Approach
Participants work from a defined oil and gas support task, such as preparing a draft maintenance request from an approved engineering procedure. They map where the agent gets its information, what it can prepare and who verifies the result before it is used.
Synthetic or specifically approved records introduce challenges such as conflicting source material, missing context and unclear instructions. Participants use these scenarios to test the design, refine escalation rules and explain which findings need to be resolved before the workflow could be considered for operational use.
Six capabilities, and how far this course takes each one
Data Intelligence
Intelligent Monitoring
Document Intelligence
Workflow & Agent Design
Responsible Implementation
Business Transformation
Ready to take the next step?
Reserve your place, ask us a question, or arrange private training for your team.
Frequently Asked Questions
A conventional AI assistant typically responds to an individual request. An AI agent can coordinate several steps and use approved tools within defined permissions. For example, it could retrieve a maintenance procedure, check it against a work order and prepare a draft for engineer review.
Suitable candidates include retrieving approved engineering documents, summarising production exceptions and preparing draft maintenance requests. Look for tasks with reliable records, repeatable steps and a clear way to check the result. The course also considers the consequences of an error and where an engineer should review the output.
Yes. The course covers RAG at the workflow design level, using sources such as approved operating procedures and engineering instructions. Participants consider how to cite the relevant document revision and how the agent should respond when records are outdated, missing or contradictory.
Participants define who may approve each action, what evidence that person needs and when the agent must pause or escalate. For example, an agent may draft a maintenance request for an authorised engineer to check before it progresses. Conflicting records or requests beyond the agent’s permissions can trigger escalation.
Participants can complete the course without API or Python programming experience. The focus is on designing workflows, defining tool permissions and assessing proposed actions. Familiarity with oil and gas operations, maintenance or technical records helps participants apply these concepts to their work.
The course considers potential connections to computerised maintenance management systems (CMMS), enterprise asset management (EAM) systems, work-management tools, document repositories and historian-derived reports. Participants assess which information an agent needs and which tool requests would require approval. Live connectivity depends on technical feasibility and organisational authorisation.
Participants define test criteria for accuracy, source traceability, task completion and exception handling. They also consider response time, engineer review effort and incorrect tool actions. Where a baseline is available, they compare the proposed workflow with the current process to assess whether it delivers a measurable improvement.
Yes. Excentra can adapt examples to the team’s responsibilities and workflows, such as production reporting, maintenance work preparation or shift handovers. Share the roles involved and the process you want to explore so Excentra can discuss suitable scenarios. Excentra Discuss your team’s requirements with Excentra.
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