AWS published a technical walkthrough for building a multi-agent document classification system on Amazon Bedrock designed to help insurance companies automatically classify thousands of daily documents.
The system combines three specialized AI agents—one for textual analysis using Claude Haiku 4.5, one for visual pattern recognition using Amazon Titan Multimodal Embeddings, and one for validation and orchestration—to achieve higher accuracy than single-model approaches and reduce manual, error-prone classification work.
Code examples and deployment guidance are available in AWS's GitHub repository.
What happened
AWS published a technical guide demonstrating how to build a document classification system using multiple specialized AI agents on Amazon Bedrock. The solution uses three agents—a Document Analysis Agent (powered by Anthropic's Claude Haiku 4.5), a Vector Similarity Search Agent (using Amazon Titan Multimodal Embeddings), and a Validation Agent—that work together to classify insurance documents like policies, affidavits, endorsements, and regulatory forms.
Why it matters
Insurance companies manually classify thousands of daily documents, a process that is time-consuming and error-prone. Traditional single-model approaches struggle when documents contain similar terminology but serve different purposes—for example, a policy endorsement and a regulatory affidavit. The multi-agent approach combines textual reasoning with visual layout pattern recognition to achieve higher accuracy and provide confidence scores that flag edge cases for human review, helping companies avoid compliance violations and processing delays.
What to watch
The complete code is available in AWS's GitHub repository. The solution requires Python 3.14 or later, AWS CLI 2.0 or later, and access to Anthropic's Claude Haiku 4.5 and Amazon Titan Multimodal Embeddings models on Amazon Bedrock. Users should review Amazon Bedrock pricing and follow cleanup instructions to avoid ongoing charges.
Ask the AI about this article →
Manual document classification in insurance is both a bottleneck and a compliance risk. Documents that share similar terminology—such as a policy endorsement and a regulatory affidavit—can be easily misclassified when using only textual analysis, leading to missed compliance obligations or delays in claims processing. AWS's multi-agent approach addresses this limitation by breaking the classification task into specialized subtasks, each handled by an agent optimized for a particular aspect of document analysis.
The architecture's core innovation is the combination of two complementary analysis modes. Claude Haiku 4.5 excels at understanding complex legal language and extracting semantic meaning from text—what documents say. Amazon Titan Multimodal Embeddings, by contrast, analyzes visual and structural patterns—how documents look. By using FAISS (Facebook AI Similarity Search) to compare incoming documents against pre-trained visual templates, the Vector Similarity Search Agent can identify document types even when textual content varies. The Validation Agent then synthesizes results from both specialists, resolving disagreements and assigning confidence scores that allow companies to flag uncertain cases for human review rather than forcing a binary automated decision.
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