AI-native observability needs new SLIs beyond latency and error rate
rseroter · x · 2026-10-02
An InfoWorld article argues traditional monitoring fails for AI-native systems: an assistant can return responses in under a second with 99.9% availability while still fabricating answers — dashboards show green while users get a "semantic failure."
The piece proposes a new set of SLIs for LLM applications:
- Task accuracy: a completed request isn't a completed task
- Token-generation latency: distinct from classic response time
- Hallucination rate: syntactically valid but factually wrong output
- Bias drift: output tendencies shifting over time
- Prompt-injection resilience: safety failures never show up as HTTP 503
- Retrieval quality: correctness along RAG dependency chains
- Cost per successful task: efficiency tied to outcomes
Core thesis: non-deterministic behavior, multi-step reasoning, retrieval dependencies and tool calls require extending existing observability stacks to measure whether systems are useful, grounded, safe, efficient and resilient — not merely reachable.
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