Multi-Agent System for Dynamic Business KPI Selection, Evaluation and Quantification Based on Oracle EBS

The growing complexity of enterprise resource planning (ERP) systems necessitates intel-ligent approaches for dynamically identifying and evaluating key performance indicators (KPIs) that accurately reflect organizational performance. This paper proposes a mul-ti-agent architecture for dynamic KPI management over Oracle E-Business Suite (EBS). The core design combines a dynamic multi-agent analytics layer, an extendable dedicated EBS KPI Model Context Protocol (MCP) server layer, and a data layer. The dynamic multi-agent analytics layer defines a set of independent large language model (LLM) agents, each re-sponsible for a specific task determined by the business requirements of a particular com-pany. The EBS KPI MCP server layer defines the tools required to access and transform Oracle EBS data and exposes them to the AI agents in the upper layer. Above these layers is the user layer, where the user actively participates in the process through a hu-man-in-the-loop approach. Based on this general architecture, we proposed and imple-mented, as a proof of concept (PoC), a multi-agent system for dynamic business KPI selec-tion, evaluation, and quantification, in which three distinct agents for KPI selection, KPI quantification, and KPI forecasting were instantiated within the multi-agent analytics lay-er. This demonstrates the practical applicability of the proposed general architecture. The study contributes to intelligent business analytics by showing how coordinated LLM agents can automate KPI lifecycle activities within ERP ecosystems, enabling adaptive, data-driven performance management aligned with evolving organizational needs.