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AI-Augmented Loyalty: Integrating LLMs and Agentic AI into Rewards Platforms for Personalization at Scale

by Muralidharan Lakshmanan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 136
Year of Publication: 2026
Authors: Muralidharan Lakshmanan
10.5120/ijca97298325f6eb

Muralidharan Lakshmanan . AI-Augmented Loyalty: Integrating LLMs and Agentic AI into Rewards Platforms for Personalization at Scale. International Journal of Computer Applications. 187, 136 ( Aug 2026), 16-21. DOI=10.5120/ijca97298325f6eb

@article{ 10.5120/ijca97298325f6eb,
author = { Muralidharan Lakshmanan },
title = { AI-Augmented Loyalty: Integrating LLMs and Agentic AI into Rewards Platforms for Personalization at Scale },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2026 },
volume = { 187 },
number = { 136 },
month = { Aug },
year = { 2026 },
issn = { 0975-8887 },
pages = { 16-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number136/ai-augmented-loyalty-integrating-llms-and-agentic-ai-into-rewards-platforms-for-personalization-at-scale/ },
doi = { 10.5120/ijca97298325f6eb },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-08-20T21:55:02.670067+05:30
%A Muralidharan Lakshmanan
%T AI-Augmented Loyalty: Integrating LLMs and Agentic AI into Rewards Platforms for Personalization at Scale
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 136
%P 16-21
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the biggest challenges for modern loyalty programs is that they do not offer customized engagement and have fixed rewards. This study examines how the rewards landscape is evolving and the impact it has on the use of Large Language Models (LLMs) and Agentic AI. The use of autonomous agents that are able to understand subtleties in behavioral data enables hyper-personalization at scale. The proprietary synthetic data set contains 361 unique customer scenarios and is used to model actual engagement metrics and transactions. Key tools used are Python, open-source language models for contextual sentiment analysis and a custom-built multi-agent framework for autonomous decision making. The methodology has designed and optimized an intelligent and self-correcting architecture which dynamically adjusts promotional copy and reward valuations, thus replacing the traditional and rigid rule-based engines. The findings show that agentic orchestration has a significant impact on redemption velocity, as well as on consumer retention and loyalty when compared to traditional approaches. With the integration, platforms can process unstructured consumer feedback and historical activity and deliver customized experiences, without manual intervention. The implications of this research are that AI-empowered loyalty systems can provide a blueprint for the future-ready loyalty landscape, where efficiency and customer lifetime value are maximized. The results show that a loyalty environment based on moving beyond the classical concept of demographic segmentation and moving toward an individual, context-aware and responsive one is effective.

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Index Terms

Computer Science
Information Sciences

Keywords

Agentic AI Large Language Models Customer Loyalty Rewards Hyper-Personalization Behavioral Analytics

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