Build an AI-Powered A/B testing engine using Amazon Bedrock

AWS has released a reference architecture for building an AI-powered A/B testing engine that fundamentally upgrades traditional experimentation. The system, built on Amazon Bedrock, Amazon ECS, DynamoDB, and the Model Context Protocol (MCP), replaces the standard practice of randomly assigning users to test variants. Instead, it uses Claude Sonnet as an AI decision engine to evaluate real-time user context—like device type, referral source, and past behavior—to intelligently assign each user to the variant most likely to resonate with them. This context-aware approach aims to cut through statistical noise and surface meaningful performance signals much earlier in the testing cycle.

The core innovation is the AI decision prompt fed to Amazon Bedrock for each variant request. The prompt packages the user's current session context, their behavioral history from DynamoDB, patterns from similar users, and real-time performance data for all experiment variants. Claude Sonnet, equipped with native tool use via MCP, processes this information to select an optimal variant, effectively personalizing the experiment in real-time. The serverless architecture, fronted by CloudFront and secured within a VPC, is designed for scalable, adaptive experimentation that can help marketers and product teams converge on a confident winning variant significantly faster than waiting for traditional statistical significance.