arXiv Open Access 2025

Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics

Akshara Prabhakar Roshan Ram Zixiang Chen Silvio Savarese Frank Wang +3 lainnya
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Abstrak

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

Topik & Kata Kunci

Penulis (8)

A

Akshara Prabhakar

R

Roshan Ram

Z

Zixiang Chen

S

Silvio Savarese

F

Frank Wang

C

Caiming Xiong

H

Huan Wang

W

Weiran Yao

Format Sitasi

Prabhakar, A., Ram, R., Chen, Z., Savarese, S., Wang, F., Xiong, C. et al. (2025). Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics. https://arxiv.org/abs/2510.17797

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Tahun Terbit
2025
Bahasa
en
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arXiv
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Open Access ✓