From Paper to Structured JSON: An Agentic AI Workflow for Compliant BMR Digital Transformation.
| Authors | Bhavik Agarwal et al. |
| Year | 2026 |
| Venue | EACL 2026 |
| Paper | View on ACL Anthology |
| PDF | Download |
Abstract
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Engineering Breakdown
Plain English
This paper describes an agentic AI workflow that automates the conversion of unstructured paper documents (specifically BMR—Basal Metabolic Rate or similar regulatory documents) into structured JSON output while maintaining compliance requirements. The system uses AI agents to handle the multi-step extraction and validation process, addressing the practical problem of digitizing legacy paper-based workflows at scale.
Key Engineering Insight
The critical engineering insight is using agentic workflows (multi-step AI reasoning with checkpoints) rather than end-to-end neural models for document transformation—this allows compliance validation steps to be explicit and auditable, which is essential when regulations require traceability of how data was extracted and transformed.
Why It Matters for Engineers
Many enterprises still rely on manual document processing for regulatory compliance because fully automated systems lack transparency and auditability. This agentic approach lets teams automate routine extraction while keeping human-verifiable decision points, reducing manual work without sacrificing the compliance audit trail that regulators demand.
Research Context
Prior work treated document-to-JSON conversion as a pure ML extraction problem, but regulated industries need compliance guardrails built into the pipeline itself. This paper advances the field by showing how to compose AI agents with explicit validation steps, bridging the gap between high-accuracy ML extraction and the deterministic, auditable workflows that production compliance systems require.
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