AMBGS: A Layered Multi-Agent Architecture for Autonomous Business Growth Through Coordinated Intelligence and Adaptive Decision Support

Abstract

Small and medium enterprises (SMEs) are now using automation in marketing, selling, and customer support; nevertheless, traditional automation technologies tend to work as isolated units without enough collaboration among different enterprise operations. This approach can lead to a delay in reaction, duplication, missed sales opportunities, and reliance on human resources. In order to overcome these problems, the present paper offers the Autonomous Multi-Agent Business Growth System (AMBGS) which is an integrated multi-agent system with marketing, sales, and customer support agents. In addition, the paper suggests a Confidence-Ranked Agent Scheduling Algorithm (CRASA) in order to choose agents depending on the relevance to the area of operation, workload, readiness, and efficiency of previous task execution. A common vector database is provided in order to get constant access to relevant product information, company policy, and prices. A Predictive Decision Intelligence Module (PDIM) is regularly trained with business performance rolling metrics. In contrast to automation systems with a dashboard architecture, AMBGS allows making high-confidence decisions and sending them straight to the respective agents for implementation. This system was analyzed on the basis of six months of simulated SME operations data. As shown by experiments, AMBGS was able to decrease the average reaction time from 38 s for a rule-based solution to 4 s and raise the lead conversion rate by 9.4 percentage points against a single agent baseline solution. In addition, there was a reduction in human interaction with the system by 11% in the analyzed tasks. Thus, the potential of coordination among multi-agents for automation is demonstrated. In addition, the research proposes four testable hypotheses, a formal throughput model for CRASA, and an evaluation process.