Adit Abraham describes documents as a difficult input boundary for AI agents. PDFs, images and spreadsheets encode meaning through layout, merged cells, charts and handwritten additions, while downstream agents can amplify extraction mistakes across multiple steps.
Adit Abraham explains a hybrid parsing approach combining efficient computer-vision layout detectionComputer vision enables software to analyze visual inputs such as images or video and extract information about their contents or structure. with vision-language reasoningA vision-language model jointly processes images and language so it can describe, answer questions about or act on visual information.. Simple tables can use Markdown, complex structures need richer representations, and retrieval may require a different representationRetrieval is the process of selecting relevant stored information and returning it to an AI system for the current task. from the one used for final reasoning. Correct extraction should preserve even erroneous source contentIntelligent document processing uses AI to classify, extract and validate information from documents for downstream workflows. rather than silently rewrite it.
Adit Abraham describes routing and splitting documents to constrain context, using iterative tools to interpret charts, and checking for silent losses such as dropped table rows. His central recommendation is to evaluate each component and the final agent outcome, monitor production drift and retain source-location information for verifiable answersData provenance records where information came from, how it changed, and which people, systems, or processes handled it.. Vendor performance claims are attributed, and promotional customer counts and contact requests are omitted.
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