Implement vector-prompt document classification using Amazon Bedrock

Amazon Bedrock now supports a multi-agent approach to document classification, addressing the limitations of traditional single-model methods. This system orchestrates three specialized AI agents: a Document Analysis Agent powered by Anthropic’s Claude Haiku 4.5 for textual reasoning, a Vector Similarity Search Agent utilizing Amazon Titan Multimodal Embeddings for visual pattern recognition, and a Validation Agent that ensures quality and consistency through cross-validation. Using the Strands Agents SDK, these agents operate independently within their expertise and collaborate via an orchestrator to produce accurate, explainable classifications.

The architecture leverages the strengths of foundation models and techniques available on Amazon Bedrock, enabling insurance companies to automate the classification of policies, affidavits, endorsements, and regulatory forms efficiently. The system processes each document by extracting text content, analyzing layout and visual features, and synthesizing the results to generate a final classification with confidence scores. This multi-agent approach has demonstrated superior accuracy compared to traditional methods, especially on complex legal and structured documents.

Implementation involves configuring foundation models, developing specialized agents for textual and visual analysis, and orchestrating their collaboration through the Validation Agent. The process includes setting up Amazon Bedrock inference profiles, deploying the agents with appropriate prompts, and integrating vector similarity search with FAISS. The solution also incorporates safeguards such as content filtering and human review triggers to ensure compliance and reliability.

This approach exemplifies how multi-agent workflows can enhance document processing accuracy, providing a scalable and explainable solution for insurance and other regulated industries. The complete implementation is available in the AWS GitHub repository, with guidance on deployment, testing, and cleanup to avoid ongoing costs.