Journal of International DBA Studies - GGU

AI-Enabled Training Micro-Agents Longitudinal Effects on Adoption, Learning Efficiency, and Human Oversight

Smrite Goudhaman — 2026-03-25

This study reports a longitudinal assessment of micro-agents facilitated by AI in a front-line hospitality environment. It compares a supervised micro-agent deployment in 2024 with a scaled organizational deployment in 2025. Deployment maturity is the independent variable, while objective learning platform trace metrics - completion rate, assessment performance, and time-on-task - that comprise the dependent variables of this study. An exposure-adjusted active employee model is utilized to reduce potential biases due to a high employee turnover rate. A decrease in completion rate is found from 100% (656/656) in the supervised micro-agent deployment to 86.82% (8,505/9,796) in the scaled micro-agent deployment. A two-proportion z-test shows a significant difference (z = 11.52, p < .001) with a decrease in completion rate by 13.18% (95% CI [12.51, 13.85]). This decline is consistent with normalization effects commonly observed when controlled pilot interventions transition to scaled operational environments. Assessment performance increases from M = 80.10 (SD = 21.55) to M = 83.38 (SD = 23.14) with a small effect size (Welch's t (1108.70) = 4.29, p < .001; Cohen's d = 0.14; 95% CI [1.78, 4.79]). A decrease in mean time-on-task is found from 10.30 minutes (SD = 11.53) to 5.98 minutes (SD = 7.82) with a moderate efficiency effect (Welch's t(971.64) = -10.88, p < .001; Cohen's d = -0.52; 95% CI [-5.09, -3.53]). The findings provide empirical evidence regarding the effectiveness of AI-enabled micro-agent frameworks within frontline organizational learning environments. It links longitudinal behavioral traces with micro-agent frameworks, providing a replicable model for assessing the effectiveness of AI-enhanced organizational learning systems.

Reciprocal Enablement of Data Centers and AI Agents: From Silicon Foundations to Sentient Operations

Ratheesh Venugopal — 2026-03-25

Artificial intelligence (AI) agents and data centers are in a symbiotic relationship of mutual enablement. AI agents and autonomous goal-oriented systems, which are able to perceive, reason, and act, are becoming more and more coordinated and specifically as related to mechanical, electrical, controls, and IT (MECIT). Simultaneously, hyperscale and edge data centers deliver the silicon, networks, storage hierarchies, thermal envelopes, and governance needed to scaffold and enable agentic systems to operate at scale in real time. To address this newer phenomenon, this article consolidates a cross-knowledge base (computer science, operations research, energy systems, and international business policy) and cross checks it with current industry facts to (a) explain the processes through which AI agents achieve efficiency, resiliency, sustainability, and security of data centers; (b) analyze how data center structures, supply chains, and instituting frameworks facilitate increasingly capable AI agents; and (c) appraise managerial, financial, and policy implications over the global digital infrastructure. Examples of case studies related to reinforcement-learning (RL) cooling optimization, carbon-aware scheduling, liquid-cooled AI clusters, multi-agent enterprise orchestration, and carrier-neutral interconnection fabrics are provided. Through these sample cases, we posit that agentic automation and carbon-conscious compute are complementary and facility-scale innovations (liquid cooling, power-dense racks, and edge-to-cloud fabrics). The paper ends with research and practice agenda implications based on quantifiable KPIs (e.g., PUE, WUE, partial PUE, embodied carbon per server, outage rates) and governance anchors (NIST AI RMF, ISO/IEC 42001, EU AI act).