Multi-Agent Based Automation of Handwritten Field and Delivery Note Digitization

Manual transcription of handwritten field notes and delivery receipts creates a critical data latency and error bottleneck, delaying lot traceability, impeding FSMA 204 compliance, and obscuring real-time inventory quality. A custom automation workflow uses specialized computer vision and LLM agents to extract structured data—lot IDs, weights, origins, temperatures—directly from images or scans. This transforms a 48-hour manual process into minutes, enabling immediate posting to ERP systems like SAP or Oracle and providing the real-time data foundation for downstream spoilage prediction and recall execution.

Implementation integrates with mobile field apps for capture and uses an orchestrator like LangGraph to manage the agent sequence, validation rules, and exception routing. The architecture must include human review gates for low-confidence extractions and audit trails for compliance. By connecting directly to ERP (e.g., SAP S/4HANA) and Warehouse Management Systems, this workflow creates immediate operational leverage: reducing data entry labor by over 80%, accelerating financial reconciliation, and providing the accurate, timely data required for dynamic freshness scoring and waste reduction programs.

This workflow automates the ingestion of unstructured, handwritten field documents—the primary bottleneck in achieving granular, farm-to-fork traceability. Manual data entry from paper logs is slow, error-prone, and creates critical data gaps that undermine recall precision and freshness modeling. By implementing a multi-agent digitization pipeline, processors and distributors can capture lot-level provenance, quality metrics, and shipment details at the source, transforming paper trails into auditable digital records. This directly reduces labor costs, accelerates data availability for downstream systems, and improves the accuracy of FSMA 204 reports and spoilage risk scores.

Implementation requires orchestrating specialized agents within a framework like LangGraph, each with discrete responsibilities and fallback logic. The Vision Agent, built on a fine-tuned handwriting OCR model, extracts raw text. The Validation Agent then uses an LLM with retrieval-augmented generation (RAG) to structure the data against known entities (e.g., farm IDs, lot numbers) and apply business rules. Exceptions are routed to a human-in-the-loop interface with the original image and extraction confidence scores. The final ERP Integration Agent posts validated transactions, ensuring the digital twin of the physical shipment is created automatically. This architecture must include robust monitoring for model drift in handwriting styles and data quality metrics to maintain operational reliability.

Phase 1 establishes the core document processing pipeline, delivering a 60-80% reduction in manual data entry within 90 days. We deploy a LangGraph orchestrator to manage specialized agents for CV-based form detection, handwriting recognition (using cloud or on-prem OCR services), and LLM-based entity extraction. This initial workflow integrates with a staging database, allowing for parallel human review via a simple UI. The focus is on proving accuracy on high-volume document types and building trust in the automated output before connecting to live ERP systems like SAP S/4HANA or Oracle Cloud.

Phases 2 and 3 introduce complexity and scale. Phase 2 adds validation agents that cross-reference extracted data (e.g., lot numbers, supplier IDs) against master data in the ERP, automatically flagging discrepancies. It also implements a retraining pipeline for the vision and LLM models based on reviewer corrections. The final phase integrates the workflow with upstream field mobile apps and downstream financial systems for automated goods receipt and invoice matching, creating a closed-loop, touchless process. Each phase includes defined success metrics—extraction accuracy, throughput time, reduction in exceptions—ensuring ROI is measured and the build remains aligned with operational priorities.

Operationalizing this workflow requires strict governance to manage data quality and exception handling. The architecture must embed validation agents that cross-check extracted data against known master data (e.g., farm IDs, product SKUs) in the ERP. Confidence scores below a defined threshold automatically route documents to a human-in-the-loop review queue within a platform like ServiceNow or a custom dashboard. This control layer prevents erroneous data from polluting financial postings and traceability records, directly protecting against costly reconciliation efforts and compliance gaps.

Rollout should follow a phased, location-specific pilot, starting with a single high-volume farm or receiving dock. This allows for tuning extraction models on specific handwriting styles and document formats. Operational monitoring dashboards must track key metrics: extraction accuracy rates, human review queue volume, and system-to-post cycle time. Integrating with existing change management and IT service management processes ensures support and continuous improvement, transforming a manual data-entry bottleneck into a scalable, governed digital pipeline.

Manual transcription of handwritten field notes and delivery documents is a major operational bottleneck, creating data latency, entry errors, and compliance gaps in food traceability. This multi-agent workflow automates digitization using specialized computer vision and LLM agents to extract structured entities like lot numbers, weights, and origins. The business value is direct: it eliminates repetitive clerical labor, accelerates data availability for traceability reporting, and ensures downstream ERP systems like SAP or Oracle reflect accurate, real-time intake data, improving inventory accuracy and recall readiness.

Implementation requires a pipeline where an orchestrator, built with frameworks like LangGraph, routes documents to a vision agent for handwriting OCR and an LLM agent for entity extraction. The validation agent cross-references extracted data against master supplier and product lists, flagging discrepancies for human review via a configured UI. Approved data is posted via integration agents to the ERP's goods receipt or inventory modules. Critical controls include confidence scoring for extractions, exception routing logic, and immutable audit logs for FSMA 204 compliance, ensuring the automation layer is both efficient and defensible.