LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment

TL;DR

This paper reviews how large language models and AI agents are revolutionizing enterprise data analysis and system deployment, emphasizing new frameworks, security, and efficiency challenges in AI-driven enterprise solutions.

Contribution

It introduces a systematic approach to integrating LLMs and AI agents into enterprise data analysis and deployment, highlighting innovative frameworks and addressing key challenges.

Findings

Enhanced semantic querying over enterprise knowledge bases.

Frameworks enabling complex query understanding and multi-agent collaboration.

Discussion of security, deployment, and computational challenges.

Abstract

The rapid progress in Generative AI and Agent technologies is profoundly transforming enterprise data management and analytics. Traditional database applications and system deployment are fundamentally impacted by AI-driven tools, such as Retrieval-Augmented Generation (RAG) and vector database technologies, which provide new pathways for semantic querying over enterprise knowledge bases. In the meantime, data security and compliance are top priorities for organizations adopting AI technologies. For enterprise data analysis, SQL generations powered by large language models (LLMs) and AI agents, has emerged as a key bridge connecting natural language with structured data, effectively lowering the barrier to enterprise data access and improving analytical efficiency. This paper focuses on enterprise data analysis applications and system deployment, covering a range of innovative frameworks, enabling complex query understanding, multi-agent collaboration, security verification, and computational efficiency. Through representative use cases, key challenges related to distributed deployment, data security, and inherent difficulties in SQL generation tasks are discussed.