Transparent, Evaluable, and Accessible Data Agents: A Proof-of-Concept Framework

2025-09-28

AI Summary

To address the challenge that non-technical users struggle to effectively access enterprise data warehouses via natural language, this paper proposes a modular data agent framework that bridges the semantic gap and ensures decision transparency. Methodologically, it integrates a multi-layered reasoning pipeline—comprising intent parsing, rule-based constraint enforcement, and statistical context modeling—with a domain-specific business rule engine and an automated evaluation framework, enabling accurate NL-to-SQL generation, traceable explanations, and quantification of behavioral bias. Key contributions include: (i) the first incorporation of statistical context into LLM-driven data agents to enhance analytical credibility; and (ii) an end-to-end auditable pipeline supporting business rule validation and dynamic query quality assessment. Empirical evaluation on an insurance claims system demonstrates significant improvements in the reliability, explainability, and practical utility of LLM-based agents—particularly in high-risk scenarios.

Technology Category

• Natural Language Processing: Safety and Robustness
• Data Mining & Knowledge Management: Intelligent Query Processing
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

This article presents a modular, component-based architecture for developing and evaluating AI agents that bridge the gap between natural language interfaces and complex enterprise data warehouses. The system directly addresses core challenges in data accessibility by enabling non-technical users to interact with complex data warehouses through a conversational interface, translating ambiguous user intent into precise, executable database queries to overcome semantic gaps. A cornerstone of the design is its commitment to transparent decision-making, achieved through a multi-layered reasoning framework that explains the "why" behind every decision, allowing for full interpretability by tracing conclusions through specific, activated business rules and data points. The architecture integrates a robust quality assurance mechanism via an automated evaluation framework that serves multiple functions: it enables performance benchmarking by objectively measuring agent performance against golden standards, and it ensures system reliability by automating the detection of performance regressions during updates. The agent's analytical depth is enhanced by a statistical context module, which quantifies deviations from normative behavior, ensuring all conclusions are supported by quantitative evidence including concrete data, percentages, and statistical comparisons. We demonstrate the efficacy of this integrated agent-development-with-evaluation framework through a case study on an insurance claims processing system. The agent, built on a modular architecture, leverages the BigQuery ecosystem to perform secure data retrieval, apply domain-specific business rules, and generate human-auditable justifications. The results confirm that this approach creates a robust, evaluable, and trustworthy system for deploying LLM-powered agents in data-sensitive, high-stakes domains.

Problem

• Enabling non-technical users to query complex data warehouses through conversational interfaces
• Providing transparent decision-making with interpretable reasoning and business rules
• Ensuring system reliability through automated evaluation and performance benchmarking

Innovation

• Modular architecture for natural language data access
• Multi-layered reasoning framework ensuring transparent decision-making
• Automated evaluation framework with performance benchmarking