Why AI Agents Need Extraordinary Data

Unstructured CEO Brian Raymond’s ebook argues that document-extraction accuracy is critical for reliable AI agent workflows because errors compound across sequential steps, with 99% accuracy per step potentially causing failures in roughly four out of ten 50-step workflows, while 99.9% accuracy substantially reduces that risk. It contends that agents cannot easily compensate for flawed table data as retrieval-augmented generation systems might, since extracted values directly influence later actions and decisions. The ebook examines changing expectations for document processing between 2024 and 2026, the difficult document cases that can undermine extraction, tests of three extraction methods in a 25-step workflow, and a six-point specification for evaluating agent-ready document data pipelines.

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