Observability for AI-Native Systems: New SLIs Beyond Latency and Error Rate
InfoWorld, Wednesday, September 30th, 2026
Defines AI-specific SLIs such as task accuracy, hallucination rate and cost per successful task for LLM systems.
An AI assistant can return fast HTTP 200 responses with 99.9% availability and still give users fabricated answers, so traditional dashboards miss semantic failures.
This article defines SLIs for AI-native systems - task accuracy, token-generation latency, hallucination rate and groundedness, bias drift, prompt-injection resilience, retrieval quality and cost per successful task - with formulas for each.
It recommends layered evaluation that combines deterministic tests, sampled human review, user feedback and calibrated model-based judges, and splitting latency into retrieval, time-to-first-token and generation phases.