Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Document Type
Event
Start Date
24-11-2025 4:00 PM
Description
E-commerce web shoppers need fast, reliable responses to a variety of requests: account modifications, order tracking, or policy inquiries. Businesses must address user queries in a fast and efficient manner, or else lose customers. Multi-agent AI models boast the ability to answer customer questions and act upon consumer queries without outside intervention. However, research is sparse as to how agentic models can transfer benefit to large commercial software stacks under realistic commercial load. We sought to ask whether a multi-agent AI architecture can effectively handle commercial-scale e-commerce customer service tasks. Moreover, we investigated how a multi-agent AI architecture compares to traditional single-agent customer service solutions in handling complex e-commerce tasks. Our team developed a multi-agent AI architecture using specialized Claude Haiku agents coordinated through LangGraph, with a React frontend and PostgreSQL DB-Kafka backend. Testing will compare our multi-agent system against a single-agent baseline to evaluate effectiveness in handling complex customer service requests. Preliminary results have shown that an agentic AI architecture significantly increases query-response correctness for customer requests.
Included in
UC-1244 Agentic AI For Intelligent Customer Communication
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
E-commerce web shoppers need fast, reliable responses to a variety of requests: account modifications, order tracking, or policy inquiries. Businesses must address user queries in a fast and efficient manner, or else lose customers. Multi-agent AI models boast the ability to answer customer questions and act upon consumer queries without outside intervention. However, research is sparse as to how agentic models can transfer benefit to large commercial software stacks under realistic commercial load. We sought to ask whether a multi-agent AI architecture can effectively handle commercial-scale e-commerce customer service tasks. Moreover, we investigated how a multi-agent AI architecture compares to traditional single-agent customer service solutions in handling complex e-commerce tasks. Our team developed a multi-agent AI architecture using specialized Claude Haiku agents coordinated through LangGraph, with a React frontend and PostgreSQL DB-Kafka backend. Testing will compare our multi-agent system against a single-agent baseline to evaluate effectiveness in handling complex customer service requests. Preliminary results have shown that an agentic AI architecture significantly increases query-response correctness for customer requests.