Claude AI for Document Management: Multi-Agent Document Classification on Amazon Bedrock

Organizations process large volumes of documents every day, including contracts, insurance policies, affidavits, regulatory forms, and other business records. Accurately classifying these documents is an important part of business process automation, particularly in financial services and insurance.

Traditional automated classification systems can work well when document categories are significantly different. The task becomes more challenging when different document types contain similar terminology or share common structural characteristics.

AWS demonstrates how this challenge can be addressed with a multi-agent architecture built on Amazon Bedrock that analyzes both the textual content and visual structure of documents.

How the solution works

The architecture combines three specialized AI agents orchestrated using the Strands Agents SDK.

The Document Analysis Agent analyzes document text. It uses Anthropic Claude Haiku 4.5 on Amazon Bedrock to understand content, legal terminology, metadata, and other linguistic characteristics.

The Vector Similarity Search Agent focuses on visual analysis. Amazon Titan Multimodal Embeddings transforms documents into vector representations, allowing the system to compare their visual characteristics with known document patterns.

The Validation Agent acts as the orchestrator. It compares the outputs of the two specialized agents, evaluates confidence levels, and produces the final classification.

As a result, the system considers both what a document says and how the document looks.

Why vector analysis matters

Different document categories can contain similar words and legal terminology while having significantly different structures.

For example, an insurance document and a regulatory filing may use similar language. Text-based classification alone might therefore not provide enough information to reliably distinguish them.

Amazon Titan Multimodal Embeddings can capture visual and structural characteristics such as layouts, tables, fields, formatting conventions, and other document patterns.

In the AWS implementation, FAISS vector similarity search is used to quickly compare new documents against previously known visual patterns.

The role of AI agents

Instead of asking a single model to handle the entire classification workflow, the solution distributes responsibilities across specialized agents.

The textual agent focuses on content, the vector agent analyzes document structure, and the Validation Agent evaluates the results produced by both systems.

When the agents agree on the classification, the system can produce a result with a high confidence score.

When results conflict or confidence is insufficient, the document can be automatically flagged for human review.

This creates a human-in-the-loop approach that combines automation with additional oversight for complex or ambiguous cases.

AWS test results

AWS evaluated the architecture using a limited dataset of 20 insurance documents across three document classes.

In this test, an Amazon Textract and keyword-based approach achieved 25% accuracy, Amazon Comprehend achieved 25%, and Amazon Bedrock Data Automation achieved 70%.

The multi-agent architecture combining textual and vector analysis correctly classified all documents in the test dataset.

AWS notes, however, that the evaluation used a limited dataset. Production accuracy can vary when working with larger and more diverse document collections.

Security and control

For production environments, AWS recommends complementing the architecture with Amazon Bedrock Guardrails.

Organizations can apply PII redaction, restrict the topics agents are allowed to process, and enable model invocation logging to maintain an auditable request and response history.

The Validation Agent can also automatically flag documents for human review when classification confidence falls below a predefined threshold.

Where this approach can be used

Multi-agent document classification can be useful across insurance, banking, legal services, government, and other industries that need to automatically process large volumes of documents.

Combining Anthropic Claude Haiku 4.5, Amazon Titan Multimodal Embeddings, vector similarity search, and the Strands Agents SDK makes it possible to classify documents based on both their meaning and their visual structure.

This architecture demonstrates a practical use case for Amazon Bedrock and AI agents in automating complex document-processing workflows.