RESOLVE X: AI-POWERED AUTONOMOUS CUSTOMER SUPPORT AGENT

Abstract

Customer support is an essential component of modern digital businesses, but traditional support systems often depend heavily on human agents, predefined rules, and static knowledge bases. As the number and complexity of customer queries increase, organizations face challenges such as long response times, inconsistent answers, repeated queries, and high operational costs. RESOLVE X is proposed as an AI-powered autonomous customer support agent designed to provide intelligent, context-aware, and automated assistance to customers. The system combines Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), natural language processing, and agent-based decision-making to understand customer requests and generate relevant responses. The primary objective of RESOLVE X is to move beyond conventional chatbot behavior by enabling the system to understand the intent of a customer, retrieve relevant information, reason over the available context, and determine the appropriate action. Instead of depending only on predefined question-answer pairs, the system can retrieve information from organizational documents, FAQs, product manuals, policies, and knowledge bases. RAG helps ground generated responses in available business information, reducing the possibility of unsupported or hallucinated answers. Recent research has demonstrated the practical value of RAG-based assistants for customersupport applications. RESOLVE X also introduces autonomous agent capabilities that allow the system to perform multi-step support workflows. The agent can classify customer intent, retrieve relevant information, formulate an answer, use authorized tools or APIs when required, and decide whether a query can be resolved automatically or should be escalated to a human representative. Agentic-RAG research has shown that routing, retrieval validation, and response generation can be combined to improve the quality and relevance of customer-service interactions. The proposed system is designed with continuous evaluation, monitoring, security, and human escalation as important components. Customer conversations can be analyzed to identify unresolved issues, frequently asked questions, and areas where the knowledge base requires improvement.