Lesson 1073 of 2116
A/B Testing Agents in Production
Agent improvements need A/B testing to validate. The testing methodology differs from traditional product A/B testing.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2A/B testing
- 3agent improvement
- 4experimentation
Concept cluster
Terms to connect while reading
Section 1
The premise
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
Key terms in this lesson
End-of-lesson quiz
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