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

Key points

Current autonomous agents lack domain specialization and seamless integration, making it difficult for enterprises to turn exponentially growing unstructured data into actionable insights.
Existing deep research systems are often opaque black boxes, lacking transparency, auditability, and real-time controllability, which makes them a poor fit for dynamic enterprise environments.
Most current systems are designed for open-domain web data and do not adequately handle heterogeneous, proprietary internal enterprise sources such as databases and files.
A modular multi-agent architecture coordinates a master research agent, specialized retrieval agents for the web, academic sources, GitHub, and LinkedIn, a visualization agent, and a Reflection mechanism for adaptive query decomposition and execution.
Through a Research Todo Manager that maintains a human-readable todo.md file, it introduces steerable context engineering, allowing real-time natural-language human intervention to dynamically adjust agent plans and priorities.
It uses an extensible Model Context Protocol (MCP)-based tool ecosystem to integrate domain-specific tools such as file analysis and NL2SQL agents, enabling reasoning over diverse proprietary enterprise data sources.

External research summaries. These are not HDATF publications or measured product results.