The content discusses the evolution of AI agent stacks, highlighting the current challenge of optimizing deployed agents without rebuilding them from scratch. It introduces AgentCore Optimization as a solution for running controlled A/B experiments and evaluating live agent performance.
A Towards AI article explains counterfactual evaluation methods (IPS, SNIPS, and Doubly Robust) for ad ranking models. These techniques estimate model performance from logged data without A/B tests, critical for recommendation systems in retail.
This content describes an A/B test on raxxo.shop using three Magnific-generated images and Vercel Edge Config to evaluate the impact of a tighter crop on Click-Through Rate (CTR). An hour-long setup led to one variant outperforming the control by 18.4% after 11 days, with the article providing the exact wiring and schema.
The article criticizes Optimizely's exorbitant pricing for A/B testing, with annual costs ranging from $36,000 to over $400,000, even for seemingly simple features like multi-armed bandit tests. It highlights how these prices are prohibitive for startups and disproportionate to the actual cost of the underlying algorithms.