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

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@article{1719435,
      author = {Venkatesh Gundu},
      title = {Agentic Artificial Intelligence for Personalized Customer Experience Optimization in Retail: Algorithms, Architectures, and Production Implementation},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {3573-3585},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719435.pdf},
      abstract = {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.},
      keywords = {Agentic AI, Contextual Bandits, Two-Tower Networks, Survival Analysis, Dynamic Pricing, Reinforcement Learning, Feature Stores, Multi-Agent Orchestration, Retail Personalization, Algorithmic Fairness.},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1719435}
  }