Evolving DeepSearch Agent: Iterative enhancements for scalable, high-recall enterprise search

Search in enterprise settings is uniquely challenging—documents are siloed, context is implicit, and user queries are often ambiguous. At Box, we built the DeepSearch Agent, a multi-step, agentic retrieval system powered by LangGraph, to address these complexities. Unlike traditional search systems, DeepSearch orchestrates embedding and keyword based retrieval, LLM-based re-ranking, semantic filtering, and metadata extraction in a dynamic, modular flow. Each component was iteratively improved and deployed in production, enabling rapid experimentation and measurable improvements in answer recall. This talk walks through our journey evolving DeepSearch from static rerankers to a fully agentic system, including how we built evaluation loops, debug tools, and fallback strategies to make it reliable at scale. Attendees will leave with architecture patterns and practical lessons for building composable, AI-powered retrieval systems tailored to real-world enterprise usecases.

Roy Shubhro

Box