Automation Workflow for Document Management System (DMS) Tagging and Archiving

Manual filing of submissions, quotes, and binders into systems like SharePoint or OnBase is a major operational bottleneck, consuming underwriter time and creating compliance risk from misfiled documents. A custom workflow automates this by deploying agents that ingest documents from email, portals, and PAS outputs. These agents use layout-aware OCR and LLMs to extract key metadata—policy number, effective date, line of business, broker—and apply pre-configured tags. This eliminates repetitive clerical work, reduces filing errors by over 90%, and ensures every document is instantly retrievable for audit or renewal, directly improving underwriter productivity and operational governance.

Implementation integrates with the existing DMS via its API, using a LangGraph orchestrator to manage the classification, extraction, and policy-checking agents. Critical controls include validation against the DMS metadata schema, with exceptions routed to a human queue in ServiceNow. The workflow enforces records retention schedules by applying lifecycle tags at ingestion, automating disposal. Observability is built in, logging every action to an immutable audit trail for compliance. Rollout is phased, starting with standardized binder documents before expanding to complex submission packets, ensuring data quality and user adoption.

Manual filing of submissions, quotes, and binders into a Document Management System (DMS) is a significant operational bottleneck, consuming underwriter and administrative time while introducing compliance risk from misfiled or untagged records. A custom automation workflow eliminates this repetitive work by deploying specialized agents to extract metadata, apply retention policies, and execute filing actions directly into systems like OnBase or SharePoint. The operational upside comes from redirecting skilled labor to risk evaluation, ensuring 100% records compliance, and creating an instantly searchable audit trail for internal and regulatory review.

Implementation requires an orchestrator like LangGraph to manage the sequential and parallel agent tasks, starting with a classifier agent that identifies document type from layout and content. A metadata agent then uses LLMs and regex to pull key fields like policy number and effective date, which are validated against the Policy Administration System (PAS). The retention policy agent cross-references extracted dates with configured rules, while a filing agent handles the final API call to the DMS. Critical controls include human-in-the-loop gates for high-value binders, immutable logging of all tagging decisions, and reconciliation jobs to catch any sync failures with the core PAS, ensuring the system of record remains authoritative.

Phase 1 focuses on the highest-volume, lowest-risk document flow: ingesting and classifying final, approved binders and policies from the Policy Administration System (PAS). An orchestrator, built with LangGraph, triggers on policy issuance, extracts metadata (policy number, insured, effective date), and files the document in SharePoint with consistent tags. This initial win automates a repetitive, error-prone manual task, freeing underwriting assistants for higher-value work and creating an immediate, searchable audit trail for the most critical documents.

Phase 2 expands to the full submission lifecycle, automating the ingestion of broker submissions, initial quotes, and underwriting worksheets. This introduces complexity, requiring a human-in-the-loop review queue for low-confidence classifications and integration with a records management system like OnBase to apply retention schedules. Phase 3 finalizes the architecture with advanced governance, integrating legal hold triggers from e-discovery platforms and deploying the workflow across all lines of business, ensuring compliance and defensibility at scale.

A production-grade DMS automation workflow must embed controls at each step to manage risk and ensure regulatory compliance. The architecture begins with a secure ingestion trigger from policy administration systems like Guidewire or document submission portals. An orchestrator, built on LangGraph or a similar framework, coordinates specialized agents for classification, metadata extraction from ACORD forms and financial PDFs, and validation against underwriting guidelines. Every action is logged with a timestamp, user/system ID, and decision rationale to an immutable audit store, creating the foundation for explainability and exam readiness.

Phased rollout is critical for managing change and validating system accuracy. Phase 1 targets low-risk, high-volume documents like auto ID cards for shadow processing, comparing AI outputs to manual filing. Phase 2 expands to commercial submissions, integrating human-in-the-loop review gates for low-confidence classifications. Phase 3 activates full retention policy enforcement, automatically applying legal hold flags and purge schedules. Each phase requires monitoring dashboards for exception rates, processing latency, and user feedback, ensuring the workflow reduces operational filing labor by over 80% without compromising records management integrity.

The workflow's business value is direct: it eliminates the manual, error-prone filing that consumes 15-25% of an underwriter's time post-bind. By automatically extracting metadata (policy number, effective date, line of business) and applying retention tags, you create a searchable audit trail, reduce compliance risk, and free skilled staff for revenue-generating work. The architecture must integrate with your PAS (e.g., Guidewire), document ingestion APIs, and the DMS (e.g., SharePoint) while enforcing strict data governance.

Implementation begins by instrumenting your PAS to emit bind events, triggering the orchestrator (e.g., LangGraph). Agents are deployed as containerized services: one generates final documents, another uses an LLM for classification, and a third applies retention policies. A human review queue handles low-confidence classifications. The final step is integrating observability—logging all actions to a system like Datadog for performance monitoring and creating immutable audit logs defensible for regulatory exams.