Hybrid AI-Agent Driven Process Optimization Framework For Enterprise Decision System

The scope of enterprise reporting systems has grown significantly as far as data size, dashboard interactiveness, and visual analytics are concerned. However, in most companies, these systems operate as a static support for making decisions instead of an active decision system. The issue is that even though the dashboards detect patterns, anomalies, and trends, the user should understand these results and trigger the next steps. This implies some time lag, inconsistencies, and dependency on human decision-making. Thus, in this article, we propose a mixed AI-agent framework of enterprise decision systems where the intelligence ability of artificial intelligence is complemented by the execution capabilities of intelligent agents through staged development. The framework involves data collecting, cleaning, transformation, creation of dashboards, analysis with the help of machine learning algorithms for predictions, classifications, clustering, and detecting anomalies, and finally, the use of agents that translate model outputs into workflow activities such as approval, escalating, prioritizing, and notifying. The primary idea of this research is to split the process of intelligence generation and actions taking at different process stages, thereby increasing the modularity, interpretability, and effectiveness. However, it retains its practical applicability and relevance of dashboard analysis. The paper discusses the conceptual framework, technical design, model types, logic of process mapping, criteria of assessment, governance issues, and the possibility of enterprise implementation of the proposed approach