Enterprise Deep Research: Steerable Multi-Agent Deep Research for
  Enterprise Analytics
Authors: name: Akshara Prabhakar; name: Roshan Ram; name: Zixiang Chen; name: Silvio Savarese; name: Frank Wang; name: Caiming Xiong; name: Huan Wang; name: Weiran Yao
Published: 2025-10-20 17:55:11

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