Online Metrics to Enhance Human-Artificial Agent Collaboration Efficiency: A Narrative Literature Review

Machines have traditionally served as tools to fulfill human requirements; however, the rapid advancement of artificial intelligence has enabled the development of autonomous systems capable of functioning as fully integrated teammates. These agents can share information, assume roles, and execute tasks within collaborative environments. Effective Human–Artificial Agent collaboration, achieved through the integration of complementary cognitive and operational capabilities, has demonstrated improvements in overall team performance across multiple domains, including industrial robotics, healthcare, and augmented reality. Nevertheless, achieving both optimal performance and interaction fluency remains a significant challenge. Real-time monitoring of tasks, intentions, and constraints of human and artificial partners is still limited, and the application of quantifiable online metrics for this purpose is underexplored. This narrative review systematically examines online metrics derived from behavioral, physiological, and interaction-based approaches, discussing their potential to enhance adaptive mechanisms and optimize team fluency in H–AA collaboration.