Monitoring and Observability in Deployed AI

In the context of AI systems, traditional Application Performance Monitoring (APM) often misses failures because these systems can produce seemingly successful outputs with 200 OK HTTP responses, hiding underlying issues like hallucinations or policy drift. This playbook outlines a comprehensive approach to AI observability, emphasizing the need for a layered instrumentation stack that begins with capturing traces before adding evaluation metrics and runtime guardrails. It recommends sampling strategies that prioritize high-risk traffic and setting alert thresholds based on quality metrics, rather than just latency or error rates, to catch issues that aggregate metrics might mask. The approach also advocates for a careful rollout of observability changes across development, staging, and production environments to prevent configuration errors. Tools like Galileo's platform are suggested to help operationalize this workflow by providing visibility, evaluation, and control, including features like multi-step decision path visualization and cost-effective, scalable evaluations.

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