Implement vector-prompt document classification using Amazon Bedrock

This article demonstrates how to build a multi-agent document classification system using Amazon Bedrock that combines textual analysis with vector similarity search to achieve 100% accuracy on insurance document classification.

• Multi-agent architecture orchestrates three specialized agents: Document Analysis Agent using Claude Haiku 4.5, Vector Similarity Search Agent using Amazon Titan Multimodal Embeddings, and Validation Agent for quality assurance
• Document Analysis Agent performs textual reasoning to classify insurance documents into POLICY, AFFIDAVIT, or MISCELLANEOUS categories
• Vector Similarity Search Agent uses FAISS for visual pattern recognition and layout analysis to complement textual analysis
• Validation Agent cross-checks specialist outputs, resolves disagreements, and flags edge cases for human review
• Multi-agent system achieved 100% accuracy compared to 25% for Textract/Comprehend and 70% for Amazon Bedrock Data Automation
• Solution uses Strands Agents SDK for orchestration with agents-as-tools pattern enabling modularity and transparency
• Includes production safeguards using Amazon Bedrock Guardrails for PII redaction and content filtering

The multi-agent approach combines specialized AI agents' strengths to deliver accurate, explainable document classification suitable for compliance-sensitive insurance workflows.