Problems including going over budget, being behind schedule, safety risks, a lack of workers, and inadequate project coordination are common for construction companies. Although BIM and conventional automation have improved certain tasks, they cannot make judgments as circumstances change or transfer information between processes. A novel method of process automation is agentic artificial intelligence (AI). It is capable of autonomous perception, thought, planning, and action. In contrast to rule-based systems, agentic AI systems are goal-driven agents that can work together, learn in real time, and adjust to changes in their surroundings. This study explores the potential applications of agentic AI for critical construction jobs, including intelligent scheduling, supply chain coordination, self-controlling machinery, quality assurance, safety hazard predictions, and site monitoring. A layered design with modules for sensing, perception, decision-making, execution, and feedback is recommended in order to achieve full automation. The study also examines the effects of performance on productivity, safety, cost-effectiveness, and quality assurance. A comprehensive assessment of organizational, ethical, technological, and regulatory issues is conducted in order to pinpoint adoption barriers. The results show that strong governance structures, employee training, and digital infrastructure may significantly increase the durability and effectiveness of building projects.