Retail Resilience Engine: An Agentic AI Framework for Building Reliable Retail Systems With Test-Driven Development Approach

Abstract

System reliability and operational resilience are two critical success factors in the retail industry that are directly connected to customer satisfaction and business sustainability. Staying competitive in today’s dynamic and rapidly evolving market requires rapid adaptability. However, it contradicts the reliability and resilience. This paper proposes an innovative solution, the Retail Resilience Engine (RRE), to establish a balance between these success factors and market demand. It is a unique framework that combines Test-Driven Development (TDD) with a Large Language Model (LLM). This framework follows the state-of-the-art Agentic-AI architecture. It effectively evaluates the decision-making process at rapid speed in retail by incorporating diverse factors, including inventory management, demand forecasting, and customer feedback. As a result, the system reliability is improved significantly. The experimental analysis of the proposed framework shows its decision-making is similar to human experts with a similarity index of 97.5%. It further proves the reliability of the system. The framework also scales effectively, maintaining high accuracy, precision, recall, and F1 scores across varying dataset sizes. The robustness analysis of the system demonstrates the agility enhancement across diverse retail domains, ensuring consistent performance with accuracy exceeding 90% across all tested scenarios. The integration of a creative filtering mechanism further enhances the performance of the RRE framework by preventing 98.2% of the irrelevant inputs. Overall, the proposed RRE framework demonstrates the impressive potential to transform retail systems by enhancing reliability, scalability, and decision-making quality through an Agentic-AI approach.