Adaptive Agentic Intelligence Framework For Autonomous Multi-Step Decision Making In Dynamic Computing Environments

Abstract

The autonomous intelligent system is becoming more expected to do dependable multi-step decision making in dynamic computing environment with a changing context, uncertain information and varying operational requirements. Traditional rule-based, planning-based, and available agentic models tend to fail to uphold a steady decision quality in case the environmental circumstances shift during task performance. This paper suggests an Adaptive Agentic Intelligence Framework which combines context sensitive perception, goal oriented reasoning, adaptive planning, and ongoing feedback in order to facilitate autonomous multi-step decision making. The framework is dynamic and thus handles its decision strategy as per the changes in the environmental factors at runtime, thus handling sequential tasks effectively. The experimental methodology is a simulation approach used to test the proposed framework to apply to the representative dynamic computing conditions. Decision Success rate (DSR) is considered the core performance measure to evaluate the framework, and it is complemented by Task Completion Time, Decision Efficiency, Adaptation rate and Resource Utilization. The results of the experiments indicate that the proposed framework is more successful in decision making, quicker, efficient in decision making, more adaptable and with reduced computational costs as compared to traditional autonomous agent frameworks. These results underscore the adaptive agentic intelligence efficiency in enhancing autonomous decision making in dynamic situations. The new framework has a solution that is scalable and adaptable to meet future generation intelligent computing systems that are in need of reliable and context sensitive and adaptive multi-step decision making.