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Amazon releases Bedrock AgentCore guide to build self-improving document processing

IDC research estimates document‑related challenges cost each information worker $19,732 per year and cut productivity by 21.3%. Companies often spend up to 30% of staff time on document tasks, with manual entry error rates of 1–4%.

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Key points

  • IDC reports document work costs $19,732 per worker annually and reduces productivity by 21.3%
  • AgentCore workflow can process a new document type in under 20 minutes and improve speed to 1 min 30 sec on repeat
  • Estimated monthly cost for 10 training and 1,000 processed docs is $58, with $0.055 per doc for model inference

The AWS blog post shows how to assemble an agentic IDP system on Amazon Bedrock AgentCore that classifies, extracts, validates and learns from business documents. Eight specialized agents work in two graph‑based workflows—identification, training for new formats, and automation for known formats—using Amazon Textract, S3 Vectors, Aurora DSQL and other AWS services. A demo processes a new purchase order in 3 minutes 49 seconds, then reprocesses a similar order in 1 minute 30 seconds after the system saves the extraction instructions.

The solution runs serverless; for 10 training documents and 1,000 processed documents per month in US‑East, the estimated cost is about $58, with foundation‑model inference accounting for roughly 85% of spend at $0.055 per document. The guide includes deployment steps, cost estimates and best‑practice tips for extending the system to other document types.

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  1. Amazon releases Bedrock AgentCore guide to build self-improving document processingthis story
Full story from AWS Machine Learning Blog · by Erik Cordsen primary sourceOpen source ↗

Build self-improving document processing using Amazon Bedrock AgentCore

AWS Machine Learning Blog · 14 October 2026

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This text was published by AWS Machine Learning Blog and written by Erik Cordsen. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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