A/B Testing Agents in Production

Agent A/B testing requires methodology adapted to non-deterministic outputs and trajectory-level evaluation.

What AI does well here

• Test on representative real traffic, not synthetic
• Define success metrics that match user outcomes (not just intermediate signals)
• Run long enough to capture variance in agent behavior
• Maintain user experience parity across variants (no degraded variant should hit users disproportionately)

What AI cannot do

• Substitute A/B testing for actual quality measurement
• Predict agent variance in advance
• Eliminate the cost of running experiments

Practice this safely

Use a small project example from your own work. The useful move is to compare the AI's draft against your goal, sources, and constraints before you trust it.

1. 1Ask AI to explain A/B testing in plain language, then underline anything that sounds uncertain or too broad.
2. 2Give it one detail from "A/B Testing Agents in Production" and ask for two possible next steps plus one reason each step might be wrong.
3. 3Check agent improvement against a trusted source, teacher, adult, expert, or original document before you use it.