Accurate prediction of the disease state and identification of candidate biomarkers from high-dimensional RNA sequence (RNA-seq) data is a major challenge in precision medicine. Conventional Machine Learning (ML) pipelines for transcriptomic analysis typically involve sequential steps of preprocessing, feature selection, hyperparameter tuning, and evaluation. These pipelines often rely on either manual trial-and-error or exhaustive grid searches, which are time-consuming and may overlook the optimal configurations. In this study, we benchmarked a standard ML workflow against an agentic AI framework for RNA-seq based disease prediction and candidate biomarker identification. The ML baseline includes classifiers such as Support Vector Machine, Logistic Regression, and Random Forest optimized via conventional grid search. In contrast, agentic AI workflow employs Large Language Model (LLM) agents to autonomously orchestrate a cascade of downstream steps- preprocessing, class balancing, model selection, parameter optimization, and feature interpretation. Across multiple experimental setups, the agentic approach achieved a consistent 2-5% improvement in predictive accuracy, with notable gains in precision and recall even under class imbalance. A novel algorithm is proposed to identify candidate biomarkers by systematically ranking the top-contributing RNA features and then cross-referencing them with two public biomarker repositories. Our findings demonstrate that agentic AI not only improves model’s prediction performance but also streamlines RNA-centric transcriptomic analysis, offering adaptability and actionable insights for RNA-based biomarker studies.