Amazon Bedrock is being used to implement a multi-agent document classification system that helps insurance companies accurately categorize thousands of daily documents, including policies, affidavits, endorsements, and regulatory forms. 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. Source: awsml
The solution architecture combines multiple specialized AI agents, each optimized for specific aspects of document analysis, working together through coordinated orchestration. This multi-agent approach addresses the limitations of single-model classification by using the unique strengths of different foundation models and techniques available through Amazon Bedrock. In our testing, single-model approaches struggled with edge cases and complex documents that require both textual and visual analysis. Multi-agent systems address this by breaking down the classification task into specialized subtasks, with each agent focusing on its area of expertise. We chose the Strands Agents SDK because it implements the agents as tools and our classification system needs an orchestrator that can invoke specialized agents as callable tools. The Validation Agent calls each specialist, compares their classifications, and resolves disagreements without custom orchestration code. This pattern offers several advantages: Modularity, Transparency, Flexibility, and Reliability. Source: awsml
At the core of the architecture is the Validation Agent, which acts as an orchestrator and implements the agents as tools pattern using the Strands Agents SDK. This agent provides quality assurance through cross-validation and confidence scoring. It compares the outputs from both the Document Analysis Agent and Vector Similarity Search Agent. It identifies areas of agreement and disagreement, then generates a final classification with an associated confidence score. This validation step helps the system maintain high accuracy while flagging edge cases for human review. The Validation Agent coordinates with two specialized agents: the Document Analysis Agent, which uses Anthropic’s Claude Haiku 4.5 on Amazon Bedrock for advanced textual reasoning and legal language interpretation, and the Vector Similarity Search Agent, which uses Amazon Titan Multimodal Embeddings G1 to convert documents into high-dimensional vector representations for visual similarity search. Source: awsml
