Reliability Testing
Learn to ensure AI agents perform consistently and handle failures gracefully
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0 / 5 completedWhy Reliability Testing Matters
Benchmarks measure what your agent can do at its best. Reliability testing reveals what happens when things go wrong: edge cases, API failures, malformed inputs, network timeouts. Production agents face messy reality, not clean test suites. Reliability testing ensures your agent handles chaos gracefully.
The Reality Gap
- •Benchmarks: Clean inputs, perfect conditions, single metrics
- •Production: Typos, edge cases, failures, timeouts, unexpected formats
- •The Gap: Agents that score 90% on benchmarks might fail 50% in production
Interactive: Explore Reliability Dimensions
Click on each dimension to understand what to test and why it matters:
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Reliability Beats Peak Performance
Users prefer an agent that's consistently 85% good over one that's sometimes 95% and sometimes 70%. Unreliable agents force users to double-check everything, defeating the purpose of automation. Focus on reducing variance and worst-case failures, not just optimizing average-case performance.