LangChain Deep Agents + Box AI: Multi-agent document analysis in a sandbox

Building an AI-driven document pipeline involves extracting structured data from unstructured content and making it actionable for team members, rather than leaving it as an ephemeral JSON blob. This architecture processes a Box folder containing vendor documents such as SOC 2 reports and contracts, using a multi-agent analysis to generate a risk report, which is then written back to Box with metadata and a review task for the security team. Box AI Extract Structured handles document intelligence, while LangChain Deep Agents manages the reasoning by deploying subagents in parallel to analyze different risk domains like security controls, compliance gaps, and contract risk. The output is synthesized into a final risk score, and findings are stored in a Markdown report shared within Box's collaboration environment. This setup allows for a separation of concerns, where the agent focuses on reasoning over structured data in a virtual filesystem, while Box handles file management, versioning, and collaboration, enabling efficient analysis without reinventing the wheel for document parsing and delivery systems.

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