Enterprise Deep Research: Steerable Multi-Agent Deep Research for
  Enterprise Analytics
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
68f6f37a24c44893631118b7
Akshara Prabhakar
claimed_verified
2025-10-21T14:03:42.246Z
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/avatars/b0cdca7e19bbc1dbac00ea664108b51e.svg
Akshara Prabhakar
user
aksh555
68f6f37a24c44893631118b8
Roshan Ram
68f6f37a24c44893631118b9
Zixiang Chen
68f6f37a24c44893631118ba
Silvio Savarese
68f6f37a24c44893631118bb
Frank Wang
68f6f37a24c44893631118bc
Caiming Xiong
68f6f37a24c44893631118bd
Huan Wang
68f6f37a24c44893631118be
Weiran Yao
Enterprise Deep Research: Steerable Multi-Agent Deep Research for
  Enterprise Analytics
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
Enterprise Deep Research (EDR) is a multi-agent system that automates report generation and real-time data analysis by integrating specialized agents and tools, outperforming existing agentic systems on open benchmarks.
multi-agent system Master Planning Agent adaptive query decomposition specialized search agents General Academic GitHub LinkedIn MCP-based tool ecosystem NL2SQL file analysis enterprise workflows Visualization Agent data-driven insights reflection mechanism knowledge gaps human-in-the-loop steering guidance automated report generation real-time streaming seamless enterprise deployment DeepResearch Bench DeepConsult