Implement vector-prompt document classification using Amazon Bedrock
Learn how to build a multi-agent document classification solution on Amazon Bedrock using the Strands Agents SDK. Three specialized agents combine textual analysis with Claude Haiku 4.5 and visual similarity search with Amazon Titan Multimodal Embeddings to accurately classify insurance documents such as policies and affidavits.
Implementing vector-prompt document classification using Amazon Bedrock can significantly improve accuracy and efficiency for insurance companies dealing with thousands of daily documents, such as policies, affidavits, endorsements, and regulatory forms. Traditional manual classification is both time-consuming and prone to errors, while conventional automated methods often fail to distinguish between documents that appear similar but serve different purposes, leading to compliance violations and processing delays.
The proposed solution employs a multi-agent approach using Amazon Bedrock's advanced AI capabilities. This approach involves three specialized agents working in tandem: 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 independently using its area of expertise before collaborating via an Orchestrator to deliver comprehensive results.
The Validation Agent acts as the orchestrator, coordinating the other agents and ensuring high accuracy through cross-validation and confidence scoring. It compares the outputs of the Document Analysis Agent, which leverages Anthropic’s Claude Haiku 4.5 for advanced textual interpretation and legal language understanding, and the Vector Similarity Search Agent, which utilizes Amazon Titan Multimodal Embeddings G1 for visual pattern recognition.
The latter agent converts documents into high-dimensional vectors to facilitate visual similarity search, complementing Claude Haiku 4.5's textual analysis.
The multi-agent architecture addresses the limitations of single-model classification by integrating specialized models and techniques tailored to different aspects of document analysis. By breaking down the classification task into specialized subtasks, each agent focuses on its domain of expertise, thereby enhancing overall accuracy and reliability.
To implement this solution, you will need an AWS account with permissions to access Amazon Bedrock, along with IAM permissions to create and invoke foundation models. You will require Python 3.14 or later, the AWS CLI version 2.0 or later, an IDE or text editor such as VS Code or PyCharm, Git for repository management, and the Strands Agents SDK for orchestrating the agents as callable tools.
The Validation Agent, at the core of the architecture, generates a final classification with an associated confidence score by comparing outputs from the Document Analysis and Vector Similarity Search Agents. This ensures high accuracy while flagging edge cases for human review. The architecture also incorporates FAISS for efficient vector similarity search, supporting rapid comparisons against known document templates.
By following the outlined process and utilizing the provided resources, readers can successfully deploy a multi-agent document classification system tailored to their specific needs using Amazon Bedrock.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
