A/B Testing Agents
definition
A/B testing agents involves running different agent configurations (prompts, models, tools, parameters) in parallel on real traffic to measure which performs better on production metrics. Unlike offline evals that test against curated datasets, A/B testing captures real-world performance including edge cases, user patterns, and environmental factors that test suites can't predict.
A/B testing agents involves running different agent configurations (prompts, models, tools, parameters) in parallel on real traffic to measure which performs better on production metrics. Unlike offline evals that test against curated datasets, A/B testing captures real-world performance including edge cases, user patterns, and environmental factors that test suites can't predict. The key challenge is that agent outputs are complex and multi-dimensional — a configuration might be faster but less accurate, or more correct but more expensive — requiring multi-metric analysis rather than simple conversion tracking. Understanding A/B testing for agents matters because it's the final validation layer: offline evals tell you what should work, but A/B tests tell you what actually works with real users. This concept connects to eval-driven development for the offline evaluation that precedes A/B testing, quality metrics for defining the metrics being compared, cost tracking for the economic dimension of configuration comparison, and regression testing for ensuring the "losing" variant doesn't degrade baseline performance.