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Why temperature=0 Is Not Enough for Deterministic AI Agent Testing

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A DEV Community article explains why AI agents can produce inconsistent outputs even when temperature is set to 0, making exact-string test assertions unreliable. The root cause lies in the full inference stack: floating-point arithmetic on GPUs is non-associative, and providers may change hardware, batching, or model quantization at any time. These small numerical shifts can alter token selection, causing a model to rephrase a correct answer in a way that breaks brittle tests. The article recommends replacing verbatim string comparisons with contract-based checks that validate response structure, required fields, data types, and value ranges. Using JSON Schema validation is highlighted as a practical method to catch real regressions without generating false failures from harmless rephrasing.

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Why temperature=0 Is Not Enough for Deterministic AI Agent Testing · ShortSingh