Organizations commonly rely on A/B testing to optimize user experience, messaging, and conversion flows. However, traditional A/B testing assigns users randomly and requires weeks of traffic to reach statistical significance. While effective, this process can be slow and might not fully leverage early signals in user behavior.
This post shows you how to build an AI-powered A/B testing engine using Amazon Bedrock, Amazon Elastic Container Service, Amazon DynamoDB, and the Model Context Protocol (MCP). The system improves traditional A/B testing by analyzing user context to make smarter variant assignment decisions during the experiment. This helps you reduce noise, identify behavioral patterns earlier, and reach a confident winner faster.
By the end of this post, you will have an architecture and reference implementation that delivers scalable, adaptive, and personalized experimentation using serverless AWS services.
