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
翻译:随着信息呈指数级增长,企业面临着将非结构化数据转化为连贯、可操作洞察的日益增长的压力。虽然自主智能体展现出潜力,但它们常常难以处理特定领域的细微差别、意图对齐和企业集成。我们提出了企业深度研究(EDR),这是一个多智能体系统,集成了(1)用于自适应查询分解的主规划智能体,(2)四个专业搜索智能体(通用、学术、GitHub、LinkedIn),(3)一个支持NL2SQL、文件分析和企业工作流的、基于MCP的可扩展工具生态系统,(4)一个用于数据驱动洞察的可视化智能体,以及(5)一个检测知识缺口、更新研究方向并可选择加入人在回路操控指导的反思机制。这些组件实现了自动化报告生成、实时流式处理和无缝企业部署,并在内部数据集上得到了验证。在包括DeepResearch Bench和DeepConsult在内的开放式基准测试中,EDR在无需任何人工操控的情况下,性能优于最先进的智能体系统。我们发布了EDR框架和基准轨迹,以推动多智能体推理应用的研究。代码位于 https://github.com/SalesforceAIResearch/enterprise-deep-research,数据集位于 https://huggingface.co/datasets/Salesforce/EDR-200