Oct 21, 2026 · 38 min
Document infrastructure determines whether AI agents can work
The Missing Infrastructure for AI Agents: Documents | Reducto’s Adit Abraham
Enterprise agents increasingly depend on messy files, but errors in healthcare, finance, legal, and insurance workflows can carry consequences far beyond ordinary software bugs.
- 1Reliable document understanding requires specialized parsing, evaluation, and workflow infrastructure beyond general-purpose model calls.
- 2Lower processing costs make it economical to combine documents with search, retrieval, and agentic information synthesis.
- 3High-stakes deployment still needs representative benchmarks, golden datasets, and human fallback when difficult cases exceed model reliability.
Don't miss
Abraham explains that accuracy can fall below 50% on the hardest healthcare document cases, underscoring why human fallback remains necessary.
The brief
Adit Abraham, Reducto’s co-founder and CEO, argues that AI agents cannot reliably operate in enterprise workflows while critical information remains trapped in messy PDFs, scans, spreadsheets, and office files.
The conversation traces a shift from traditional OCR to vision-language processing, then to Reducto’s specialized R1 model and the feedback loops needed to improve accuracy, speed, and cost.
Reducto’s broader workflow harness handles evaluation, versioning, redlines, and review—because parsing a page is easier than completing a loan packet or other end-to-end task.
Lower per-page costs expand the role of agents, allowing document processing to work alongside web search, knowledge-base retrieval, and broader information synthesis across enterprise workloads.
Healthcare supplies the sharpest warning: difficult patient packets can produce accuracy below 50%, making human fallback essential while systems improve for consequential decisions.
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