Build self-improving document processing using Amazon Bedrock AgentCore
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This article introduces a self-improving intelligent document processing system built on Amazon Bedrock AgentCore, designed to address the high costs and rigidity of traditional template-based methods. The solution utilizes eight specialized AI agents that collaborate to classify, extract, validate, and learn from business documents with minimal human intervention. By leveraging vector similarity in Amazon S3 Vectors, the system identifies known document types to apply stored extraction instructions or routes new formats through a training workflow. During training, agents generate prompts, extract data, and refine instructions based on validation failures, persisting this knowledge for future reuse. This autonomous learning capability significantly reduces processing time for subsequent documents of the same type, as demonstrated by a case study where processing time dropped from nearly four minutes to one and a half minutes. The architecture integrates AWS services such as Textract, Aurora DSQL, and Lambda, orchestrated via Strands Agents and monitored through AgentCore Observability. Additionally, the post provides deployment steps using AWS CDK, cost estimates, and best practices for customizing the solution for specific enterprise needs.
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