Implement vector-prompt document classification using Amazon Bedrock | Amazon Web Services
Vector-prompt classification on Amazon Bedrock helps insurance companies accurately classify thousands of daily documents: policies, affidavits, endorsements, and regulatory forms, for compliance, claims, and customer service. Manual classification is time-consuming and error-prone, while traditional automated approaches struggle with documents that look similar but serve different purposes. A policy endorsement and a regulatory affidavit might contain similar terminology, yet misclassifying them can lead to compliance violations or processing delays.
This post demonstrates how you can build a multi-agent solution using the Strands Agents SDK. The solution orchestrates three specialized agents: a Document Analysis Agent for textual reasoning, a Vector Similarity Search Agent for layout pattern recognition, and a Validation Agent for quality assurance. Each agent operates autonomously within its expertise, then collaborates through an Orchestrator to deliver results.
You will learn how to implement this multi-agent architecture for your own document classification needs, with code examples and technical guidance. This...
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