What is AI observability?

Traditional observability, which relies on logs, metrics, and traces, is insufficient for AI systems because AI products can appear operationally sound yet produce incorrect or harmful outputs. AI observability addresses this by offering a framework to continuously monitor, evaluate, and enhance AI systems, connecting production behavior tracing, output quality evaluation, and system configuration iteration into a closed loop. The need for AI observability has grown due to the complexity of multi-step AI agents and the inadequacy of traditional testing methods for large-scale AI products. Technical challenges such as managing heterogeneous and mutable trace data, performing exploratory query patterns, and scaling for large data volumes necessitate custom infrastructure, such as Braintrust's Brainstore, which combines multiple database functions into a single system. Effective AI observability platforms tightly integrate various capabilities, including tracing, evaluation, analytics, and automation, providing a seamless feedback loop essential for rapid iteration and improvement.

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