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

About

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

Akshara Prabhakar, Roshan Ram, Zixiang Chen, Silvio Savarese, Frank Wang, Caiming Xiong, Huan Wang, Weiran Yao• 2025

Related benchmarks

TaskDatasetResultRank
Deep Research Report GenerationDeepResearch Bench
Comprehensiveness49.01
54
Comparative Performance EvaluationDeepConsult
Win Rate0.7157
24
Open-Ended Deep ResearchDeepResearch Bench Open-Ended
Overall Score50.62
11
Open-ended deep research evaluationDeepResearch Bench 100 PhD-level research tasks
Comprehensiveness49.7
9
Deep ResearchDeepConsult (test)
Win Rate71.57
8
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