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

This article presents an AI-powered A/B testing engine using Amazon Bedrock that improves traditional random assignment through intelligent variant selection based on user context and behavior.

• Traditional A/B testing uses random assignment, requiring weeks to reach statistical significance
• AI engine analyzes user context, behavioral history, and similar user patterns for smarter decisions
• Amazon Bedrock intelligently orchestrates tool calls to gather only necessary data for each user
• Hybrid strategy: hash-based assignment for new users, AI-driven for returning users with behavioral data
• Model Context Protocol (MCP) provides structured access to user profiles, performance metrics, and experiment data
• System synthesizes multiple factors: device constraints, similar user patterns, engagement metrics, historical performance
• Confidence scores (0.0-1.0) reflect decision certainty based on data availability and signal alignment
• Architecture uses ECS, DynamoDB, S3, CloudFront, and VPC endpoints for serverless scalability
• Real examples show loyalty members assigned to concise CTAs, deal-seekers to incentive messaging
• Future enhancements: dynamic variant generation, multi-armed bandits, cross-experiment learning

The solution enables faster convergence to winning variants while reducing noise through personalized, context-aware assignment decisions without manual post-hoc segmentation.