A/B Testing

For growth scenarios, evaluating an agent should also incorporate "experiment win rate" — how many of the merged changes achieve statistically significant positive results in A/B tests.

In the AI development context, "running an experiment" usually refers to a controlled experiment (A/B testing or offline evaluation): holding other variables fixed and changing only one factor (such as model version, prompt template, retrieval strategy, temperature parameter), running the same batch of representative inputs and quantitatively comparing the results.

A horse-race test (A/B test) is a common practice in performance advertising: multiple creative variants are run simultaneously, market data determines which version wins, and the budget is then concentrated on the best-performing variant.

Method for validating a model swap: route half of agent traffic to the new model for a week and compare three metrics—resolution rate, CSAT and cost per conversation.

A/B testing frameworks and shadow traffic mechanisms are widely used to compare the performance of different model versions in real production environments.

The effectiveness of automated iterative testing is highly dependent on traffic volume; when traffic is insufficient, the system struggles to statistically distinguish real differences between versions

Traditional A/B testing requires running two versions simultaneously and determining the winning version through statistical significance testing after accumulating a sufficient sample size

In traditional operations, A/B testing requires manually designing schemes, setting up experiments, and waiting for data before making decisions, with cycles often measured in weeks.

Conversion-rate-driven business logic has compressed the space for design experimentation, as A/B test data tends to point toward safe design choices