Human-Guided Agentic AI Workflows for Enterprise Operational Decision-Making
Keywords:
Agentic AI, Human-in-the-Loop, Enterprise Decision-Making, Machine Learning, Random Forest, Explainable AIAbstract
This study demonstrates human-in-the-loop agentic AI workflows for making operational decisions using an organized Python-based analytical approach within this enterprise. The study examines the enhancement of decision-making processes by combining the capabilities of AI agents with human supervision in terms of accuracy, productivity and trust. The data from the enterprise has been pre-processed, visualized and analyzed by applying machine learning methods. Two models are employed: Logistic Regression and Random Forest, with observed better performance being achieved for Random Forest in dealing with a complex relationship. The result shows that hybrid systems (AI/Human) achieve higher reliability and interpretability compared with fully-automated systems. This study emphasizes the significance of Explainable (XAI) and Regulated (Injection Deception) Artificial Intelligence (AI) in the enterprise.
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