LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment
This work addresses data analysis and security problems for enterprises adopting AI technologies, but it appears incremental as it builds on existing methods like RAG and vector databases.
The paper tackles the challenge of enterprise data analysis by leveraging large language models and AI agents to generate SQL from natural language, aiming to lower access barriers and improve efficiency. It discusses innovative frameworks for complex query understanding, multi-agent collaboration, and security verification, while addressing challenges like distributed deployment and data security.
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.