Building Enterprise Applications with LLM Mod...

Building Enterprise Applications with LLM Models: A Deep Dive into RAG and Agentic Architectures

Author Name : Srikanth Vadlamani, Dr. Tushar Mehrotra

ABSTRACT The rapid advancements in large language models (LLMs) have enabled the dawn of new horizons for the integration of enterprise applications, allowing organizations to tap into tools capable of enhancing decisions, optimizing processes, and fueling customer experience. However, the overall capabilities of LLMs in the context of the enterprise remain underserved, notably in the development of their adoption in Retrieval-Augmented Generation (RAG) systems and agentic designs. RAG models, blending retrieval mechanisms and generative capacities, offer the ability to investigate vast amounts of unstructured information while maintaining generation of context-relevant outputs. Similarly, agentic architectures—that have LLMs operating in autonomous agent scenarios—offer hope for enhanced automation and decision-making in various areas of business endeavor. Despite all this, it remains a stark research gap considering the best practice, issues, and design approaches required to maximize the integration of these models with enterprise applications. Current research exists mainly to outline LLM proficiency in standalone task scenarios rather than within the live, intercommunicating systems representative of enterprise functionality. This paper aims to resolve this gap in research by investigating the use of RAG and agentic models in enterprise domains, evaluating the effectiveness of applying them to ensure the complex requirements of modern enterprises are met (e.g., scalability, security, reliability). Through case analysis and architectural conceptualization, the paper hopes to provide practical value for enterprise architects, data analysts, and business decision-makers so that they might develop and put in place reliable LLM solutions that improve functioning and create value for business operations.