Hierarchical Agentic Intelligence Framework For Explainable Multi-Step Autonomous Decision Making

Abstract

There is a growing expectation that autonomous intelligent systems can make multi-step decisions involving complex decisions and respond to dynamic environments and give transparent explanations of their behavior. Nevertheless, a number of current agentic artificial intelligence methods have drawbacks in hierarchical planning of tasks, explainability, and adaptive decision-making, which diminish their usefulness in autonomous applications in the real world. The current paper suggests a Hierarchical Agentic Intelligence Framework of Explainable Multi-Step Autonomous Decision Making that structures the autonomous thinking into hierarchical levels that handle goal decomposition, task planning, sequential decision execution, and continuous feedback. A built-in explainable decision module provides interpretable decision traces, allowing users to get insight into the decision process of every autonomous action and thus enhancing system visibility and reliability. The suggested framework is tested within a simulation-based experimental setup which consists of several autonomous decision-making scenarios with different degrees of complexity. Task Success Rate (TSR) is used to evaluate performance and is the main evaluation measure, and Description Accuracy, Planning Time, Explainability Score, and Adaptation Success Rate are also evaluated, to achieve a balanced score in effectiveness, computational efficiency, transparency, and adaptability. According to the experimental findings, the hierarchical structure proposed can better accomplish the tasks, improve the quality of decisions, shorten the time spent on planning, and increase the explicability than traditional autonomous decision-making methods do. The proposed framework offers scalable, interpretable solution to hierarchical agentic intelligence that can be used in the development of reliable and effective autonomous decision-making systems that can be used in dynamic computing contexts.