Why AI Agents Need Extraordinary Data | Unstructured

Featured RAGLLMUnstructured RAG Needed Mediocre Data. Agents Need Extraordinary Data. Oct 14, 2026 Featured RAGLLMUnstructured RAG Needed Mediocre Data. Agents Need Extraordinary Data. Oct 14, 2026 Authors Brian Raymond Founder and CEO, Unstructured In this article Join our newsletter to receive updates about our features. In this article At 99% accuracy per step, 4 in 10 agent workflows still go wrong. RAG could work around a messy table. Agents can’t. They take the value in the cell, act on it, and pass it to the next step, so one extraction error becomes the starting point for every decision after it. In a 50-step workflow, small errors compound, and the first step, turning documents into data, is where they start. At 99.9% per step, failures drop to 1 in 20. In this ebook, Unstructured CEO Brian Raymond explains why that gap matters and how to close it: what to measure, what to demand from your document pipeline, and how to test it on your own files. What's inside: What changed from 2024 to 2026, and why “good enough” stopped being good enough Five ways the hardest 10% of a document silently breaks extraction What we learned testing three extraction approaches across a 25-step workflow A six-point spec for agent-ready data you can hold any vendor to Building agents on documents? Start with the data. This form requires targeting cookies to load. To view this form, please accept targeting cookies. Join our newsletter to receive updates about our features. Related Articles Use Case Use Case: Consumer Goods Industry Jun 7, 2025 Unstructured Fine-tuning How We Taught an AI Agent to Fix Our Training Data Apr 23, 2026 Ajay Krishnan Use Case Use Case: AI Course of Action Generation and Analysis Dec 28, 2024 Unstructured