DESIGN AND IMPLEMENTATION OF A RAG-BASED AI AGENT FOR ROBO-ADVISORS

Abstract

This study focuses on the design and implementation of an agentic system based on Retrieval-Augmented Generation (RAG) architecture for automated financial advisory services (Robo-Advisors, RA). The relevance of this study lies in the knowledge limitations of large language models (LLMs) in domain-specific areas, such as the financial sector, where factual errors can lead to direct financial losses and regulatory risks. Based on a literature review, the class of tasks for a modern Robo-Advisor platform suitable for RAG-based solutions has been identified. The purpose of this research is to develop a RAG agent architecture for a Robo-Advisor service and verify its functionality on a working prototype. Functional requirements for an RA module were established to track the behaviour of financial assets within a specific investment portfolio. An analysis was conducted on the tools used to implement typical interaction scenarios with a RAG system (data acquisition, text preprocessing, vectorisation, data normalisation, LLM interaction, etc.). A software architecture was designed in which agent functions (obtaining stock prices, analysing financial news, automated reports, user communication) are reflected in separate software modules. The system is divided into an asynchronous indexing circuit and a synchronous user interaction circuit. The agent's memory is implemented as hybrid storage, where a relational database serves as the source of truth, and the vector index is a fully recoverable derivative artifact. A method for re-ranking news search results using a normalised time-of-publication coefficient is proposed. User interaction is implemented as a chatbot for a messenger. Pilot operation confirmed that the system meets the established requirements; identified shortcomings and limitations of the agent are described, along with methods for their resolution. The results of this study can be used to integrate RAG agents into full-scale Robo-Advisor services.