The evolution of document intelligence is transforming how machines interpret and process documents, moving from basic Optical Character Recognition (OCR) and Natural Language Processing (NLP) to advanced AI document parsing using large language models (LLMs). Traditional methods, like OCR, NLP, and Named Entity Recognition (NER), provided foundational tools for digitizing and organizing text but struggled with understanding document context and relationships due to their reliance on templates and rules. LLMs have introduced a significant advancement by offering zero-shot semantic understanding and deep multimodal capabilities, enabling them to interpret complex documents without prior template tuning or training. Despite their advantages, LLMs face limitations with highly structured or densely formatted documents and require extensive prompt engineering and operational considerations. The next phase, termed agentic parsing, combines LLM reasoning with modular, orchestrated components to create a dynamic workflow capable of self-correction and verification. This approach aims to transform document parsing into a system of document intelligence that not only reads but also reasons and acts on the information, paving the way for fully multimodal parsing, autonomous document agents, continuous learning, and explainable outputs.