Build self-improving document processing using Amazon Bedrock AgentCore

This article demonstrates how to build a self-improving intelligent document processing (IDP) system using Amazon Bedrock AgentCore, where specialized AI agents automatically handle document classification, extraction, validation, and learning.

• Eight specialized agents collaborate across identification, training, and automation workflows to process documents without manual template configuration
• System learns from each new document format and reuses knowledge, so similar documents process faster without retraining
• Deploy multi-agent IDP infrastructure in 15–20 minutes using AWS CDK with serverless resources (Lambda, DynamoDB, Aurora DSQL, S3 Vectors)
• Agents use Amazon Textract for text extraction, vector embeddings for document classification, and business rule validation against enterprise data
• Training workflow generates extraction instructions and refines them through validation failures; automation workflow reuses learned instructions for known document types
• Example shows first purchase order from new vendor takes 3 minutes 49 seconds; second order from same vendor completes in 1 minute 30 seconds using learned instructions
• Estimated cost approximately $58/month for 10 training and 1,000 processed documents, with foundation model inference representing 85% of costs
• Customize by defining JSON schemas for document types, building validation Lambda functions, and updating agent prompts for your business rules

This agentic approach eliminates rigid templates and manual configuration, enabling organizations to process diverse document formats from hundreds of vendors while continuously improving accuracy through autonomous learning feedback loops.