AWS describes business document processing using Amazon Bedrock AgentCore
The guide from AWS demonstrates a system with eight agents for processing business documents on Amazon Bedrock AgentCore. It routes unknown formats to a learning workflow and stores revised instructions for future use. According to the company, deployment takes approximately 15–20 minutes.
AWS published a guide to building a system that classifies business documents, extracts data and validates the results. Eight specialized agents collaborate in two graph-based workflows built using the open source SDK Strands Agents. The solution runs on Amazon Bedrock AgentCore. Deployment to an AWS account creates six infrastructure stacks through AWS CloudFormation and, according to the company, takes approximately 15–20 minutes.
The Matcher agent searches for stored instructions for similar documents. It routes an unknown format to a learning workflow, where Amazon Textract extracts the text, other agents create instructions, convert the data to JSON and validate it against a schema. In the example, the Instructions Fixer agent added a rule for converting a supplier SKU to an internal format after a validation error. The corrected instructions were stored for subsequent orders. According to AWS, the first processing run in this scenario using synthetic data took 3 minutes 49 seconds.
For a monthly volume of 10 documents in the learning workflow and 1 000 processed documents in the US East (N. Virginia) region, AWS estimates costs at approximately 58 USD per month. Model inference through Amazon Bedrock is expected to account for approximately 85 % of the costs, roughly 0.055 USD per document. According to the company, the actual cost depends primarily on the models assigned to individual agents.
The example includes a JSON schema for orders and a validation function that checks data against product and supplier records in a simulated ERP database. For your own documents, you need to replace the sample JSON schema and validation logic. When changing models, the article recommends verifying extraction quality using AgentCore Evaluations; the solution already uses AgentCore Observability. Details are available in the source article.
Why it matters
The guide shows a specific way to separate the processing of known formats from learning new ones, then reuse corrected instructions. This gives teams processing documents from different suppliers a basis for piloting automation. However, the result of the example using synthetic data does not, by itself, demonstrate quality or savings when working with their documents.
Two audiences, two different impacts
What this means
For individuals
Developers have a documented process for connecting extraction, validation and instruction correction. Adapting it to their own documents requires defining the required fields and adjusting the validation rules.
For a business
For a company handling incoming business documents, the guide provides a basis for a pilot deployment and an estimate of operating costs. According to AWS, model inference accounts for approximately 85 % of the costs in the example deployment, so model selection has a significant impact on the budget.
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