Agentic Artificial Intelligence for Personalized Customer Experience Optimization in Retail: Algorithms, Architectures, and Production Implementation

This paper provides a technically grounded examination of Agentic AI architectures deployed for retail customer experience optimization. Unlike prior work that addresses this topic at a strategic level, this paper identifies the specific machine learning algorithms, model architectures, feature engineering pipelines, and system orchestration patterns that underpin production deployments. Core topics include contextual bandit algorithms — LinUCB, Thompson Sampling, and neural bandits — for real-time offer selection; two-tower neural retrieval networks with approximate nearest-neighbor search for large-scale product recommendation; gradient-boosted demand elasticity models and reinforcement-learning pricing agents for dynamic pricing; survival analysis and deep temporal models for customer churn prediction; and LLM-based multi-agent orchestration patterns for autonomous retail workflows. The paper further addresses real-time feature store architecture, bias detection and mitigation, and guardian-agent governance for safe autonomous action execution. Two detailed production scenarios — influencer-triggered dynamic pricing and subscription churn intervention — illustrate end-to-end system integration. The intended audience is data scientists, machine learning engineers, and technical architects building or evaluating agentic retail systems.