Galileo AI: The AI Observability and Evaluation Platform

AI incidents offer valuable lessons, but capturing these lessons requires a structured approach that transforms one-off fixes into lasting system improvements. Research indicates that only about half of AI incidents lead to formal post-incident evaluations, missing critical learning opportunities. High-performing AI teams achieve better reliability by systematically learning from incidents, which involves more than just engineering fixes, as AI failures are probabilistic and context-dependent, unlike traditional IT failures. Such incidents require a cross-functional response and sophisticated monitoring to detect subtle performance degradations, such as model drift, concept drift, and fairness regressions. Elite teams, characterized by their more frequent incident reporting, are often more mature organizations with robust detection capabilities, seeing high incident counts as indicators of organizational health rather than failure. The key to improving reliability lies in a 5-phase framework: detecting and triaging anomalies, diagnosing root causes, documenting incidents with structure, designing new evaluations, and deploying these evaluations into CI/CD pipelines. This framework, supported by tools like Galileo, ensures that every incident translates into systematic, permanent enhancements, with the creation of automated evaluations as a non-negotiable part of the process.

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