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This paper evaluates the deployment of LLMs and agentic AI in the energy industry, focusing on automating tasks like reporting, compliance, and cyber-defense. It uses a structured evaluation framework to classify outputs based on traceability, reproducibility, and hallucination risk, comparing human-led interactions with autonomous agent loops. The study finds that while LLMs improve efficiency, they introduce governance risks due to lack of validation and unclear boundaries between assistance and autonomous recommendation, potentially leading to acceptance of fabricated content.
LLMs in energy operations can significantly reduce drafting time, but their unverifiable outputs and autonomous decision-making introduce governance risks that current digital assurance frameworks fail to capture.
This paper evaluates the operational deployment of large language models (LLMs) and agentic AI systems within the energy industry. The primary focus is on their role in automating reporting, compliance monitoring, decision support, and cyber-defence tasks. It explores both the measurable gains in process efficiency and the poorly understood liabilities introduced by autonomous decision-making and unverifiable outputs. The study draws on technical analysis of LLMs and agent chains such as ReAct, AutoGPT, and enterprise-tuned proprietary models. A structured evaluation framework is used to classify outputs based on traceability, reproducibility, and risk of hallucination. Benchmark comparisons are performed between human-led, prompt-based LLM interactions and autonomous agent loops with embedded task prioritisation. Use cases include automated compliance drafting, anomaly detection summaries, and initial incident response generation. Legal and audit implications are assessed with respect to digital evidence, explainability, and attribution of action. Findings reveal that while LLMs and autonomous agents significantly reduce drafting time and offer versatile adaptive outputs, they introduce governance risks rarely captured by current digital assurance frameworks. High-value operations, particularly those involving compliance-sensitive tasks, show susceptibility to misdirection when outputs are not independently verified or when agentic loops execute without escalation triggers. Observations indicate an absence of formalised validation layers, leading to potential acceptance of hallucinated or fabricated content under the false presumption of system precision. In energy sector trials, users lacked clarity on when LLMs switched from generative assistance to autonomous recommendation, thereby diffusing responsibility and undermining post-decision traceability. The study calls for strict operational boundaries and layered validation protocols before integration of LLMs into decision-critical workflows. This paper presents a risk-graded framework for evaluating agentic LLM outputs in operational settings, providing the petroleum sector with a method to align automation benefits with responsible governance practices. It is one of the first to link LLM traceability with legal auditability in energy operations.