Building systems with Langchain deep agents and Box

Deep Agents, a framework from LangChain, offers a structured approach to building complex systems by using orchestrators, sub-agents, and persistent memory to prevent the chaos often seen in multi-agent systems. In an implementation for auto loan underwriting, the orchestrator acts as a coordinator, delegating specific tasks such as document extraction, policy interpretation, and risk calculation to specialized sub-agents, each with limited and focused responsibilities. This separation of concerns ensures that each sub-agent is isolated and cannot access the full system, thus avoiding the "everything talks to everything" problem. The system supports a persistent audit trail, crucial for compliance in regulated industries, by recording every decision, calculation, and data extraction in a structured manner. Box AI is integrated to handle document intelligence, allowing for natural language queries and structured data extraction without the need for manual PDF parsing. This setup allows the orchestrator to manage workflows effectively and focus on business logic rather than infrastructure, providing a reliable and debuggable system.

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