Agentic Artificial Intelligence (AI), defined by its autonomy, reactivity, proactivity and adaptive learning capabilities, is rapidly transforming diverse sectors ranging from healthcare and finance to manufacturing, education and security. Despite its growing impact, there is limited systematic evaluation of Agentic AI performance across application domains. This study addresses the gap by using multiple machine learning models including Logistic Regression, LinearSVC, MultinomialNB, SGDClassifier, Extra Trees, Random Forest, XGBoost, LightGBM, and ensemble Voting Classifiers on a curated dataset of Agentic AI applications. The methodology integrates preprocessing, feature engineering, and model optimization, followed by rigorous evaluation using accuracy, precision, recall and F1-score. Results demonstrate that ensemble approaches, particularly the Hard Voting Classifier, achieve the highest overall accuracy (94.7%), while domain-specific performance varies. Further analysis reveals patterns of application adoption across industries underscoring the domain-dependent nature of Agentic AI deployment.
 

Agentic AI, Agentic Applications, AI agents, Healthcare, Finance, Information Technology, Ensemble learning, Autonomous systems, Robotics