{"ID":2852929,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17797","arxiv_id":"2510.17797","title":"Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics","abstract":"As information grows exponentially, enterprises face increasing pressure to transform unstructured data into coherent, actionable insights. While autonomous agents show promise, they often struggle with domain-specific nuances, intent alignment, and enterprise integration. We present Enterprise Deep Research (EDR), a multi-agent system that integrates (1) a Master Planning Agent for adaptive query decomposition, (2) four specialized search agents (General, Academic, GitHub, LinkedIn), (3) an extensible MCP-based tool ecosystem supporting NL2SQL, file analysis, and enterprise workflows, (4) a Visualization Agent for data-driven insights, and (5) a reflection mechanism that detects knowledge gaps and updates research direction with optional human-in-the-loop steering guidance. These components enable automated report generation, real-time streaming, and seamless enterprise deployment, as validated on internal datasets. On open-ended benchmarks including DeepResearch Bench and DeepConsult, EDR outperforms state-of-the-art agentic systems without any human steering. We release the EDR framework and benchmark trajectories to advance research on multi-agent reasoning applications. Code at https://github.com/SalesforceAIResearch/enterprise-deep-research and Dataset at https://huggingface.co/datasets/Salesforce/EDR-200","short_abstract":"As information grows exponentially, enterprises face increasing pressure to transform unstructured data into coherent, actionable insights. While autonomous agents show promise, they often struggle with domain-specific nuances, intent alignment, and enterprise integration. We present Enterprise Deep Research (EDR), a m...","url_abs":"https://arxiv.org/abs/2510.17797","url_pdf":"https://arxiv.org/pdf/2510.17797v2","authors":"[\"Akshara Prabhakar\",\"Roshan Ram\",\"Zixiang Chen\",\"Silvio Savarese\",\"Frank Wang\",\"Caiming Xiong\",\"Huan Wang\",\"Weiran Yao\"]","published":"2025-10-20T17:55:11Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":608044,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2852929,"paper_url":"https://arxiv.org/abs/2510.17797","paper_title":"Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics","repo_url":"https://github.com/SalesforceAIResearch/enterprise-deep-research","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}