If you've ever spent hours reformatting a CSV file, fixing broken scripts, or rerunning a reservoir model because one input was off, you're not alone. Most engineers in the energy industry know the frustration of repetitive workflows. Automation has helped us manage the volume of tasks, from production- monitoring dashboards to scripted workflows for analysis, but there are limits.
Automation works beautifully when everything is predictable. But in the real world, where data is messy and workflows are rarely linear, automation often fails. That's where agentic AI comes in.
Unlike traditional automation, agentic AI is not just about executing steps faster. It's also about building systems that can reason, adapt, and collaborate, like a junior engineer who understands the tools, knows when something looks wrong, and can explain the process.
This article explores how agentic AI differs from automation, why it matters for young professionals (YPs), and how it could reshape the way we work in oil and gas.
The Limits of Traditional Automation
Automation has been a valuable step forward in petroleum engineering. We've seen it in scripts that automatically perform sensitivity analysis and scenario forecasting, macros that clean and plot log data, and dashboards that pull production metrics daily without human effort.
These solutions save time until something changes. A script might fail if a dataset has missing headers, a log export comes in a new format, or an unexpected well shut in confuses the analysis. Suddenly, an engineer is back in the loop, fixing errors and rerunning workflows.
The weaknesses of automation in oil and gas are clear.
- Fragility: Automation is brittle when inputs don't match expectations.
- Inflexibility: Each new workflow usually requires building a new pipeline from scratch.
- Human glue: Engineers still spend significant time troubleshooting, cleaning data, and stitching together pieces of workflows.
Automation executes but it doesn't think.
What Is Agentic AI?
Agentic AI is a new approach powered by large language models (LLMs) and advanced orchestration frameworks. Instead of following rigid, pre-coded steps, agents can
Plan: Break down a high-level goal into steps.
Adapt: Handle unexpected inputs by reasoning through alternatives.
Interact: Ask clarifying questions and explain results in plain language.
Think of it this way: Automation is like a factory worker on an assembly line, they do exactly one task very efficiently, but if something goes wrong, they stop. Agentic AI is like a junior engineer, it knows how to use the available tools, can access relevant data from databases, can troubleshoot when things don't look right, and can explain the reasoning behind its choices.
The value is not just in doing tasks, but also in adapting tasks to real-world messiness.
Case Study: Production Forecasting
Let's look at a simple production-forecasting task, a workflow every reservoir or production engineer has encountered.
Automation approach: Load CSV → Clean → Fit Arps decline → Export EUR.
Works fine if the dataset is clean and the wells behave as expected.
Where automation breaks:
- Missing or mislabeled columns
- Shut-in periods that confuse the decline fit
- Wells with atypical decline patterns
An agentic AI approach
- Recognizes missing headers and infers column meanings from context.
- Detects anomalous shut-in periods and adjusts the curve-fitting strategy.
- If decline curve analysis (DCA) doesn't fit well, tries a machine learning (ML)-based forecast as a fallback.
- Summarizes results in a short report, with uncertainty ranges and explanations.
Instead of failing, the agent adapts, like a junior engineer who doesn't just stop when the script breaks but looks for alternatives. The engineer reviewing the output doesn't waste time fixing trivial issues but instead focuses on evaluating assumptions and making decisions.
Beyond Single Workflows: Connected Analysis
The production-forecasting example shows agentic AI handling one workflow, but the real potential emerges when agents can connect related tasks. Consider a reservoir engineer working with production data from 50 wells across a field.
A traditional approach requires
- Separate scripts for each data source (different operators, file formats).
- Manual quality checks for each well's production history.
- Individual DCAs.
- Separate economic evaluations.
An agentic approach could handle the full-field analysis.
1. Data Integration: The agent recognizes that Wells A-15 and B-22 have similar completion designs and cross-references their performance patterns.
2. Adaptive Analysis: When it encounters unusual production behavior in Wells C-30 through C-35, it doesn't just flag anomalies, it queries the drilling database and discovers these wells were drilled during a specific time period with different proppant specifications.
3. Contextual Reporting: Instead of generating 50 separate estimated ultimate recovery calculations, it groups wells by completion type, identifies performance drivers, and highlights which completion designs are underperforming relative to type curves. The engineer receives a comprehensive field assessment rather than a collection of individual well analyses, with clear explanations of why certain wells were grouped together and what the performance patterns suggest about completion effectiveness.
Implementation Challenges
Agentic AI shows promise but implementing it in oil and gas environments presents specific hurdles that need honest assessment.
Data integration complexity: Most companies have production data in one system (PI, WellView), geological data in another (Petrel, Kingdom), and economics in spreadsheets. Agents need reliable data connections, but many legacy systems weren't designed for Application Programming Interface (API) access.
Regulatory and audit requirements: In oil and gas, you need to document how you calculated reserves, why you made specific completion decisions, and how you assessed environmental impacts. An agent that can't provide transparent, auditable decision trails won't meet industry standards.
Safety-critical applications: While agents can handle data analysis and reporting, engineers will remain responsible for decisions affecting well integrity, environmental compliance, and personnel safety. The handoff between agent analysis and human judgment needs clear boundaries.
Integration with existing workflows: Engineers use specialized software (ECLIPSE, CMG, decline curve analysis packages) that may not easily connect with agent frameworks. Building these integrations requires both petroleum engineering knowledge and software development skills.
Cost and complexity: Small to mid-size operators may find it difficult to justify the initial investment in infrastructure, data cleanup, and staff training required to implement agentic systems effectively.
These aren't roadblocks, but they do mean that successful implementation requires careful planning, realistic expectations, and a focus on solving specific problems rather than pursuing broad automation.
Why This Matters for Young Professionals
For YPs in oil and gas, the rise of agentic AI isn't about being replaced but being empowered.
Here's why.
Repetitive tasks will shrink. Much of the "grunt work" in workflows (data cleaning, reformatting, simple analysis) can be delegated to agents.
Interpretation and judgment will matter more. Engineers will spend more time asking the right questions and validating insights.
Career opportunities will grow. YPs who can bridge domain knowledge and AI tools will be in demand.
Think of it this way: 20 years ago, Excel skills gave young engineers an advantage. Today, Python and ML provide that edge. Tomorrow, the differentiator will be your ability to work with AI agents effectively.
Agentic AI is the closest thing to augmenting expertise, even in our niche workflows. YPs who experiment with these tools early will shape how they're used in the industry.
Next Steps
The energy industry thrives on innovation, but change starts small. You don't need a full AI platform to begin exploring agentic AI. Start with building blocks.
• Automate repetitive steps with scripts.
• Experiment with open-source agent frameworks.
• Train agents on small, domain-specific workflows (e.g., log QC, DCA fitting).
The question is not whether agentic AI will drive changes in oil and gas, it's how soon and who will lead the charge.
Agentic AI won't make engineers obsolete. It will make us more impactful, freeing us from repetitive work and enabling us to focus on creativity, judgment, and decision-making.
The real question for young professionals is: Will you be ready to guide it?
