Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics
Akshara Prabhakar, Roshan Ram, Zixiang Chen, Silvio Savarese, Frank Wang, Caiming Xiong, Huan Wang, Weiran Yao

TL;DR
Enterprise Deep Research (EDR) introduces a multi-agent system that automates enterprise data analysis and report generation, effectively handling domain-specific nuances and integrating human guidance, validated on internal and open benchmarks.
Contribution
The paper presents a novel multi-agent framework, EDR, combining adaptive planning, specialized search, and reflection mechanisms for enterprise analytics, outperforming existing systems without human intervention.
Findings
EDR outperforms state-of-the-art agentic systems on benchmarks.
The system enables automated, real-time enterprise data insights.
Open-source code and datasets are provided for further research.
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…
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Code & Models
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Taxonomy
TopicsScientific Computing and Data Management · Explainable Artificial Intelligence (XAI) · Data Quality and Management
