From Ingestion to Agents: How AI Teams Build on Document Intelligence — Adit Abraham, Reducto

Building effective AI agents requires moving beyond simple RAG-based information synthesis toward robust, multi-step agentic workflows that handle unstructured, multimodal data. PDFs remain a significant bottleneck due to their complex, legacy formats, necessitating a hybrid approach that combines traditional computer vision for layout detection with Vision Language Models for semantic understanding. Implementing "agentic OCR"—a verification and correction layer—ensures high-confidence outputs by treating document processing as a dynamic, iterative task rather than a static extraction process. Furthermore, optimizing retrieval by creating natural language representations of complex tables significantly improves model performance and reduces latency. Rigorous, granular evaluation at every stage of the pipeline is essential for production-grade reliability, as agents must navigate diverse, real-world data environments where deterministic pipelines often fail.