Beyond the Vibe-Check: Quantifying Reliability for Production AI Agents

Autonomous agents handling high-stakes decisions require quantifiable reliability metrics rather than subjective confidence. This framework introduces an LLM judge that objectively evaluates agent performance against golden datasets, enabling organizations to move from 'seems to work' to defensible accuracy measurements. By implementing structured evaluation and observability, teams can secure budget approval and manage liability when deploying intelligent systems at enterprise scale.