How to Actually Test AI Systems: A Layered Strategy Using Five Open-Source Tools

Testing AI systems presents unique challenges that traditional software testing methods cannot address, primarily because LLM outputs are nondeterministic and have no single correct answer. An engineer who built five open-source tools describes a layered testing strategy that shifts the question from 'did it match?' to 'did it regress?' The base layer uses a tool called evalgate, which runs prompt and agent regression checks in CI pipelines, scoring outputs across multiple dimensions and failing builds when quality drops below a baseline. A second tool, voiceeval, addresses voice-specific failures invisible in transcripts, such as speech-to-text mishearing numbers or agents acting without confirmation. The core insight across all layers is that AI quality must be measured as a continuous signal rather than a binary pass or fail.