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A Multi-Agent Platform for Automated Enterprise Analytics and Insight Generation — arXiv:2608.18740

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.18740
License (as archived): CC0-1.0
Archived: 2026-10-18T13:44:36.862Z
SHA-256 of the archived text: cc2fc9c2697285ae725501f390eeb70d7ac64a507f5b734246dfff9b2b50e391
Stable URL of this page: https://metraipolis.com/documento/dc_74f0a5359dfd

How to cite

A Multi-Agent Platform for Automated Enterprise Analytics and Insight Generation — arXiv:2608.18740 — arXiv (en), https://arxiv.org/abs/2608.18740 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_74f0a5359dfd (Metraipolis archive dc_74f0a5359dfd, sha256 cc2fc9c2697285ae725501f390eeb70d7ac64a507f5b734246dfff9b2b50e391)

BibTeX

@misc{dc74f0a5359dfd,
  title = {A Multi-Agent Platform for Automated Enterprise Analytics and Insight Generation — arXiv:2608.18740},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.18740},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_74f0a5359dfd; sha256 cc2fc9c2697285ae725501f390eeb70d7ac64a507f5b734246dfff9b2b50e391},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.18740. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-20 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.18740 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T13:41:17.875Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).