Reliability Testing
Learn to ensure AI agents perform consistently and handle failures gracefully
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0 / 5 completedConsistency Validation
Consistent agents produce similar outputs for similar inputs across multiple runs. Inconsistency creates unpredictable user experiences: "Why did it work yesterday but fail today?" Consistency testing measures output variance, success rate stability, and behavioral patterns over repeated trials.
Why Consistency Matters
Consistency Testing Strategies
🔁Identical Input Testing
Run the same prompt 10-20 times, compare outputs
- • Goal: <10% variance in outputs
- • Check: Semantic similarity, not exact match
- • Alert: If success rate changes significantly
🎭Paraphrase Testing
Ask the same question in different ways
- • "What's 2+2?" vs "Calculate two plus two"
- • Outputs should be semantically equivalent
- • Tests robustness to phrasing variations
⏱️Temporal Consistency
Test same input at different times (morning/evening)
- • Checks for time-dependent behavior
- • Catches model version updates
- • Detects cache invalidation issues
🌡️Temperature Sweep
Test with different randomness settings (temp 0 → 1)
- • Temp 0: Should be deterministic
- • Temp 0.7: Moderate creativity, stable core
- • Temp 1: High variance expected
Interactive: Consistency Test Simulator
Run the same prompt multiple times and analyze consistency:
Define acceptable variance for your use case. Financial agents need near-perfect consistency (>95%), while creative writing agents can tolerate more variance (>70%). Monitor consistency metrics in production and alert when thresholds are breached. Track trends over time to catch gradual degradation.