INNOVATIVE APPLICATION OF AI AGENT BASED ON MARKOV DECISION PROCESS IN DYNAMIC OPTIMIZATION DECISION-MAKING FOR ENTERPRISE SERVICES

Against the backdrop of the booming digital economy, the enterprise service environment is increasingly characterized by dynamics, high uncertainty, and multi-objective collaboration. Facing core challenges such as fluctuating service demands, changing resource constraints, and rapidly iterating customer preferences, traditional static decision-making methods struggle to adapt to actual operational needs, gradually revealing their limitations. This paper focuses on key issues in dynamic enterprise service decision-making, including resource allocation, service priority setting, and precise matching of customer needs. Targeting the regional characteristics and operational pain points of enterprise service scenarios in South China, we innovatively improve the traditional Markov Decision Process (MDP) model. Leveraging the intelligent computing power and data simulation support provided by Shenzhen Qicheng Zhiyuan Network Technology Co., Ltd. through the "LiQiCloud" AI-powered Sci-Tech Innovation Policy Platform, we construct an AI agent decision-making framework that integrates Deep Reinforcement Learning (DRL) and Digital Twin technology. Furthermore, we propose an improved value iteration algorithm based on function approximation, which effectively overcomes the limitations of traditional models and significantly enhances decision-making efficiency and adaptability. Through rigorous mathematical derivation, we elucidate the theoretical foundations for state representation, action output, reward function design, and optimal policy solutions. Simulation and empirical analysis were conducted using real-world service scenarios from a small-to-medium-sized clothing e-commerce enterprise in South China (daily order volume of 1,500–2,200; peak order volume of 4,000 during major promotions in 2025; customer service team of 25). Experimental results demonstrate that the proposed model significantly outperforms traditional MDP models and manual decision-making methods in core metrics, including service cost control (reduced by 22.8%), customer waiting time (shortened by 56.1%), resource utilization (improved by 14.7%), and effective problem resolution rate (increased by 5.9 percentage points). This study provides an implementable and replicable solution for optimizing service decision-making in the digital transformation of SMEs in South China.