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OCR is the perfect example of a use case that has been dominated by legacy/brittle systems, and can be solved both accurately and cheaply by…
OCR is the perfect example of a use case that has been dominated by legacy/brittle systems, and can be solved both accurately and cheaply by applying the right amount of agentic intelligence. A proper
OCR is the perfect example of a use case that has been dominated by legacy/brittle systems, and can be solved both accurately and cheaply by applying the right amount of agentic intelligence. A properly tuned agentic OCR needs to dynamically apply extra compute to complex elements, review and correct failures, and create the right semantic meaning throughout the page. It's a task that frontier models are both overengineered for in terms of cost and latency, and struggle at over a long tail of complex edge cases. Check out this blog post by @LoganMarkewich below: https://www.llamaindex.ai/blog/ocr-is-dead-long-live-agentic-ocr OCR is dead 🪦 Long live agentic OCR! Traditional OCR makes one pass and hands back whatever text it got. Tables get flattened, charts disappear, and multi-column layouts come out scrambled. Nothing checks the output. Agentic OCR treats parsing as a loop instead: ✅ layout-aware r…
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Source: Jerry Liu (X) | 2026-10-07