Agentic AI-Enabled Intelligent Resource Optimization Framework for Construction Inventory and Waste Management

Poor material management has been one of the major causes of cost overruns, schedule delays, and environmental impact of construction projects. Most of the systems in place are descriptive but not predictive despite the increased data visibility provided by digital tools, which restricts their capacity to optimise the utilisation of resources. The paper outlines the design, implementation and field testing of an agentic artificial intelligence powered web based integrated construction inventory and waste management framework. The proposed system will have semi-autonomous decision-support agents that have the ability of contextual reasoning, adaptive learning, and dynamic threshold optimization. Eight weeks in a live setting on an active construction site showed significant improvements in the accuracy of inventory reconciliation, reliability of demand forecasting, waste minimization, and cost savings. The findings indicate that integrating agentic AI into operational processes can transform the way business relates to construction resource management to become proactive instead of being reactive.