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This paper introduces a multi-agent framework utilizing CrewAI for conversational business intelligence, where five specialized agents sequentially process natural language queries to analyze data and generate visualizations. The system achieves remarkable performance metrics, including 95.3% functional accuracy and a 93.0% hallucination-free rate, significantly outperforming a single-agent baseline. The evaluation across diverse datasets and LLM backends highlights the framework's generalizability and the critical role of specific agents in enhancing output quality.
Achieving a 95.3% functional accuracy and a 93.0% hallucination-free rate, this multi-agent platform redefines the standards for conversational business intelligence.
This paper proposes a multi-agent framework built on CrewAI [1] for conversational business intelligence. Five specialized AI agents operate in a sequential pipeline to process natural language queries, retrieve and analyze data, generate visualizations via the Model Context Protocol (MCP) [2], and deliver actionable insights. The platform features a defense-in-depth security architecture for multi-tenant data isolation and a query parameterization mechanism for transforming conversational insights into reusable dashboard components. Evaluation across 300 end-to-end test cases spanning synthetic and production enterprise datasets demonstrates 95.3% functional accuracy, a mean response latency of 24 seconds, and a response quality score of 4.52/5.0 as assessed by an LLM-as-a-Judge framework, with a 93.0% hallucination-free rate, representing a 22.6 percentage point accuracy improvement and 20.2% quality gain over a single-agent baseline. Cross-model evaluation across four LLM backends and human expert validation confirm architectural generalizability and evaluator reliability. An ablation study confirms that the Data Analysis and Report Aggregation agents are the primary drivers of output quality.
