AgentBuddy: an IR System based on Bandit Algorithms to Reduce Cognitive Load for Customer Care Agents

Abstract

We describe a human-in-the loop system - AgentBuddy, that is helping Intuit improve the quality of search it offers to its internal Customer Care Agents (CCAs). AgentBuddy aims to reduce the cognitive effort on part of the CCAs while at the same time boosting the quality of our legacy federated search system. Under the hood, it leverages bandit algorithms to improve federated search and other ML models like LDA, Siamese networks to help CCAs zero in on high quality search results. An intuitive UI designed ground up working with the users (CCAs) is another key feature of the system. AgentBuddy has been deployed internally and initial results from User Acceptance Trials indicate a 4x lift in quality of highlights compared to the incumbent system.