The growing complexity of construction and project environments has revealed the limitations of traditional BIM-based systems, particularly their dependence on human-driven decisions and fragmented data processes. This study proposes an autonomous Information Management (IM) framework powered by Agentic Artificial Intelligence (AI) to address these challenges. The conceptual framework highlights how AI agents can perceive, analyze, and act on real-time project data to improve coordination, reduce decision delays, and enhance data consistency across stakeholders. It also presents theoretical propositions linking AI integration to improved efficiency, risk management, and overall project performance. The study demonstrates that moving beyond BIM toward AI-driven autonomous systems can enable proactive project management, continuous learning, and adaptive control. The paper contributes to theory by extending IM into autonomous systems and bridging AI with construction management, while offering practical insights for industry adoption. Future research should focus on empirical validation and real-world application of the framework.
Ph.D. et al. (2026) studied this question.