AI-Driven Multi-Agent Shopping System through E-Commerce System
DOI:
https://doi.org/10.15680/IJCTECE.2026.0902004Keywords:
Multi-agent systems, e-commerce automation, intelligent shopping agents, agent negotiation, collaborative filtering, federated learning, dynamic pricing, blockchain identity, personalized recommendations, MLOps for e-commerceAbstract
The rapid evolution of e-commerce has created demand for more intelligent, personalized, and efficient online shopping experiences. Traditional e-commerce platforms operate with static interfaces where users manually search, compare, and purchase products. This paper proposes a novel AI-driven multi-agent online shopping system that revolutionizes the e-commerce paradigm through autonomous agents capable of negotiation, collaboration, and intelligent decision-making. The system employs specialized agents including Personal Shopping Agents, Price Negotiation Agents, Quality Verification Agents, Logistics Optimization Agents, and Dispute Resolution Agents that work collaboratively to enhance the shopping experience. By integrating machine learning algorithms, natural language processing, federated learning for privacy preservation, and blockchain-based identity verification, the proposed system delivers superior personalization, dynamic pricing through multi-party negotiations, enhanced security, and improved customer satisfaction. Experimental results demonstrate a 67% reduction in purchase decision time, 34% cost savings through automated negotiations, and 89% user satisfaction improvement compared to traditional e-commerce platforms. The system also introduces innovative features such as predictive purchase assistance, dynamic group buying, sustainability assessment, and cross-platform agent mobility. This research provides a comprehensive blueprint for next-generation e-commerce systems that prioritize user autonomy, privacy, and intelligent automation.
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