Implement vector-prompt document classification using Amazon Bedrock

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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 multi-agent approach combines the advanced reasoning capabilities of Anthropic’s Claude Haiku 4.5 with the visual pattern recognition of Amazon Titan Multimodal Embeddings available on Amazon Bedrock to achieve better classification accuracy. Solution overview The solution architecture combines multiple specialized AI agents, each optimized for specific aspects of document analysis, working together through coordinated orchestration. This multi-agent …

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