An AI assistant can return a response in under a second, maintain 99.9% availability and still give customers a fabricated answer. Traditional dashboards will show green because the request completed successfully. The user, however, received a semantic failure: a response that is syntactically valid but factually wrong, unsafe, biased or irrelevant.
That is why AI-native systems need observability beyond latency, error rate, throughput and saturation. LLM applications introduce non-deterministic behavior, multi-step reasoning, retrieval dependencies, tool calls and safety risks that do not appear as HTTP 503 responses. This article defines the Service Level Indicators (SLIs) that make those failure modes visible and shows how to extend an existing observability stack.
Quick answer
AI-native observability measures whether an AI system is useful, grounded, safe, efficient and resilient—not merely reachable. The core AI SLIs are task accuracy, token-generation latency, hallucination rate, bias drift, prompt-injection resilience, retrieval quality and cost per successful task.
Why traditional observability is insufficient
Traditional application monitoring assumes an explicit contract: a request either succeeds or fails, and a small number of technical signals explain most degradation. LLM systems break that assumption. The same input may produce different outputs; quality can decline after a model, prompt or retrieval update; and the response can look correct to an API client while failing the user’s actual task.
- HTTP 200, incorrect answer: the model responds successfully but invents a policy or recommendation.
- Grounding failure: a RAG system retrieves relevant documentation, but the model ignores or contradicts it.
- Tool misuse: an agent chooses a valid operational tool with the wrong arguments.
- Reasoning loop: an agent repeatedly calls tools, increasing latency and cost without progressing.
- Safety failure: a malicious prompt embedded in an uploaded document persuades the model to reveal data or ignore policy.
An effective design keeps infrastructure SLIs and AI quality SLIs together. Infrastructure tells you whether the platform is healthy. AI SLIs tell you whether the platform is trustworthy.
The AI-native SLI set
1. Response accuracy or task success rate
Response Accuracy measures the percentage of outputs that correctly fulfill the defined task. For an incident assistant, success may mean identifying the correct service owner, assembling evidence, selecting an approved runbook and escalating when confidence is insufficient. For a support bot, it may mean delivering an answer that is correct, complete and supported by policy.
Formula: Response Accuracy = correct responses / total evaluated responses × 100.
Use a layered evaluation method: deterministic tests for known cases, sampled human review for high-fidelity judgment, user-feedback signals and model-based evaluators against a written rubric. A model-as-judge should be calibrated against human evaluations rather than trusted blindly.
2. Token generation latency
End-to-end latency is too coarse for LLM applications. Split a request into queue time, retrieval time, time to first token, generation time, tool-call time and post-processing time. Token-generation latency measures the inference cost of producing output once generation begins.
Formula: token-generation latency = generation duration/output tokens.
This distinction makes incidents diagnosable. A high end-to-end latency may be a slow vector-store lookup, a provider slowdown, an overloaded tool dependency or an overlong response. Measuring time to first token and milliseconds per output token turns one vague latency alert into actionable signals.
3. Hallucination rate and groundedness
Hallucination Rate is the percentage of outputs containing claims unsupported by authoritative context. For retrieval-augmented generation, the complementary measure is groundedness or faithfulness: whether the answer can be traced to retrieved documents, telemetry or verified system data.
Formula: Hallucination Rate = ungrounded responses / total evaluated responses × 100.
Require citations or evidence IDs for high-stakes answers and score whether those sources actually support the claims. This converts “the response sounded plausible” into a measurable reliability signal. High-risk domains should use stricter thresholds and route uncertain answers to a human rather than forcing a confident response.
4. Bias drift
Bias Drift measures whether the fairness profile of outputs changes over time. This matters after model-provider changes, fine-tuning, prompt edits, retrieval-corpus updates and feedback loops. A system can remain accurate overall while becoming less helpful or more negative for a particular user group.
Formula: Bias Drift = absolute difference between the current and
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