Amazon Bedrock AgentCore provides a platform for building agentic intelligent document processing (IDP) systems that learn from new document formats without extensive upfront configuration. The solution described in the AWS Machine Learning post deploys eight specialized agents that collaborate across two graph‑based workflows. The agents perform classification, extraction, validation, and learning, allowing the system to adapt to varying layouts from suppliers such as invoices and purchase orders. Deployment is achieved through six AWS CloudFormation stacks—Core, Gateway, Aurora, Agent, UI Orchestrator, and Modern UI—using serverless and on‑demand resources, with an estimated cost of about $58 per month for a workload of 10 training documents and 1,000 processed documents in US East (N. Virginia). The solution uses Amazon Textract for text extraction, Amazon Bedrock for foundation model inference, and Lambda functions for validation against a mock ERP database. It also incorporates AgentCore Evaluations and Observability for model performance monitoring. The system stores learned extraction instructions in a vector store, enabling subsequent documents from the same vendor to be processed automatically with minimal human intervention. The post outlines how to customize schemas, validation logic, and prompts for different document types, and it warns that cleanup scripts permanently delete processed data. This agentic approach replaces manual template maintenance, reduces error rates, and improves productivity for organizations dealing with diverse document formats.
Build self-improving document processing using Amazon Bedrock AgentCore
Amazon Bedrock AgentCore enables a self‑learning intelligent document processing system that automatically classifies, extracts, validates, and refines data from business documents such as invoices and purchase orders. The solution uses eight specialized agents that collaborate across two graph‑based workflows, running on serverless AWS infrastructure and costing roughly $58 per month for a moderate workload. It eliminates the need for manual template maintenance by learning new document layouts through feedback loops.
Why it matters
By automating document processing and reducing manual configuration, this approach can lower operational costs and improve accuracy for AI‑driven enterprise workflows.