In enterprise applications, document information extraction (IE) does not operate in isolation: extracted information must match the systems of record (SoR) for the process to work. While reliable IE demands entity extraction, SoR grounding, and iterative refinement, existing methods rely on single-pass inference. To address this gap, we propose a SoR-grounded agentic pipeline and evaluate four agentic paradigms (Agentic Workflow, ReAct, Plan-Execute-Critic, and Dynamic Agent Collaboration) on real-world enterprise data. We focus on the goods receipt and invoice settlement processes as representative use cases, where delivery notes and invoices must be matched and verified against purchase order SoR. Our results show that agentic approaches generally match or exceed non-agentic baselines, with simpler paradigms matching or outperforming complex alternatives. Paired with systematic error analysis, this work provides actionable guidance for deploying agentic document AI at enterprise scale.

Vision Language Model (VLM)-based OCR-free pipelines are increasingly adopted for industrial Key Information Extraction (KIE) systems due to their simpler end-to-end architecture and reduced dependence on potentially noisy OCR outputs. However, their reliability in grounding extracted values to precise bounding boxes remains unclear, raising questions about their suitability for grounding-critical use cases. In this paper, we present a systematic study of bounding box grounding capability in both general and document-specific VLMs. Our findings reveal a consistent extraction-grounding gap across model families. We revisit the OCR system as a grounding primitive and find that delegating the grounding step to Large Language Models (LLMs), rather than VLMs, in a zero-shot manner substantially improves region-level grounding accuracy. We introduce a fine-tuned 0.6B LLM that further improves the predicted box alignment, achieving high overlap with the target field for in-domain data while maintaining low serving latency. These results position OCR-anchored grounding as a practical deployment choice for industrial KIE systems that require both extraction and precise localization.