AI-Based Retail Customer Journey Agent
SNEHALA A
Department of Artificial Intelligence
and Machine Learning
Sri Shakthi Institute of Engineering and Technology, Coimbatore, India
snehalaanandkumar22aml@srishakthi.ac.in
DURGHA S
Department of Artificial Intelligence
and Machine Learning
Sri Shakthi Institute of Engineering and Technology, Coimbatore, India
durghasivasankaran22aml@srishakthi.ac.in
ASWATHI R
Department of Artificial Intelligence
and Machine Learning
Sri Shakthi Institute of Engineering and Technology, Coimbatore, India
aswathir22aml@srishakthi.ac.in
HARINIVAS M
Department of Artificial Intelligence
and Machine Learning
Sri Shakthi Institute of Engineering and Technology, Coimbatore, India
harinivasm22aml@srishakthi.ac.in
SABARINATH R
Department of Artificial Intelligence
and Machine Learning
Sri Shakthi Institute of Engineering and Technology, Coimbatore, India
sabarinathaiml@siet.ac.in
ABSTRACT:
This project presents a robust and scalable omni-channel sales solution utilizing an Agentic AI Framework built upon a Large Language Model (LLM). By leveraging modern agentic principles, the system features a central "Sales Agent" that tokenizes customer intent and orchestrates multiple specialized "Worker Agents" to handle tasks such as inventory checks, recommendations, and payment processing. The use of a coordinated, multi-agent architecture ensures a seamless, human-like conversational journey while maintaining context across channels, making it suitable for real-time retail applications. The framework offers a practical solution to combat fragmented customer experiences, a rising concern in today's digital-first retail age. It can be integrated into web chats, mobile apps, and in-store kiosks, aiding sales teams, e-commerce platforms, and customer support in increasing Average Order Value (AOV) and boosting conversion rates.
INDEX TERMS
Agentic AI, Large Language Models (LLMs), Orchestration, Worker Agents, Tool-Use, Omni-Channel Retail, Session Continuity, Average Order Value (AOV), Conversion Rate Optimization, LangChain, FastAPI, Mock APIs, Intent Recognition, Multi-Agent Systems, Contextual Awareness, Personalized Recommendations, Fulfilment Agent, Payment Agent, Inventory Agent, Post-Purchase Support.
