IDC research shows that document-related challenges cost organizations $19,732 for each information worker annually and reduce productivity by 21.3 percent. Organizations receiving invoices from hundreds of suppliers often face layouts that vary by issuer and change without notice. Template-based systems require constant maintenance at this scale. Staff can spend up to 30 percent of their time on document-related tasks, and manual data entry error rates range from 1–4 percent. Conventional intelligent document processing (IDP) solutions address some of this burden, but they require extensive upfront configuration for each document format and break when layouts vary across vendors.

In this post, you learn how to build an agentic IDP system using Amazon Bedrock AgentCore that classifies, extracts, validates, and learns from business documents with minimal human intervention. Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. Your agents automatically handle new document formats, such as supplier invoices, purchase orders, insurance claims, and loan applications. By the end of this post, you can:

Many IDP pipelines require you to define templates, extraction rules, and validation logic for every document format upfront. When a new vendor sends a purchase order with a different layout, the pipeline breaks. This creates three core challenges:

An agentic approach addresses these challenges. Instead of static templates, AI agents analyze each document, generate extraction instructions, validate results, and learn from corrections automatically. Over time, your system builds a knowledge base of document formats, so you process similar documents with minimal additional configuration.

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