Multi-Agent AI Platform Automates Enterprise Analytics and Insight Generation

The paper presents a novel multi-agent framework designed for automated enterprise analytics and insight generation, leveraging the CrewAI platform. This system employs five specialized AI agents arranged in a sequential pipeline, each responsible for a distinct part of the business intelligence process. These agents collaboratively handle natural language queries, retrieve and analyze relevant data, create visualizations using the Model Context Protocol (MCP), and ultimately deliver actionable insights to users. Key features of the platform include a robust defense-in-depth security architecture, ensuring multi-tenant data isolation, and a query parameterization mechanism. This mechanism allows conversational insights to be transformed into reusable dashboard components, enhancing the utility and longevity of the generated analyses. Extensive evaluation across 300 end-to-end test cases, using both synthetic and production enterprise datasets, demonstrated impressive results. The platform achieved 95.3% functional accuracy, a mean response latency of 24 seconds, and a high response quality score of 4.52/5.0 as assessed by an LLM-as-a-Judge framework, with a 93.0% hallucination-free rate. This represents a significant improvement over single-agent baselines, confirming the architectural generalizability and evaluator reliability across different LLM backends and human expert validation.