AI Reliability Engineering: SLOs for Model-Backed Features

AI reliability engineering adapts site reliability engineering concepts to model-backed features by defining user-facing objectives, live-traffic service level indicators, and error budgets despite the variable outputs of AI systems. It distinguishes infrastructure measures such as availability, latency, and call errors from behavioral measures of whether responses are acceptable, which often require sampled, versioned graders calibrated against human review. The approach treats failures by providers, prompts, retrieval systems, tools, model updates, refusals, and fallback models as part of a composite feature’s reliability budget, emphasizing that HTTP success and preserved availability do not necessarily mean users received useful answers. It recommends pre-agreed budget policies that freeze feature changes after excessive failure, burn-rate rather than single-case alerting for routine probabilistic errors, explicit tracking of degraded outcomes, and separate limits for states such as refusals or fallback usage. It also calls for fault injection, detailed incident evidence including prompts, retrieved context, tool activity, model identity, and grader results, and postmortem updates to evaluation suites and controls. Citing DORA’s finding that AI adoption is associated with both faster delivery and greater instability, the discussion argues that reliable AI features require measurable acceptance criteria, ownership, and preparation for failures that traditional infrastructure metrics may overlook.

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