A/B testing provides causal evidence about which agent variant performs better in production conditions.
Key Considerations
- Sample size: Need enough users for statistical significance
- Metrics: Define success criteria before testing
- Duration: Run long enough to capture variance
Agent-Specific Challenges
- User interactions may be complex and lengthy
- Multiple metrics may conflict
- Long-term effects may differ from short-term