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Latency and error rates tell you whether an AI service is responding; they do not tell you whether it completed the user’s task correctly, safely, or with useful evidence. Keep traditional service indicators, then add outcome and behavior signals—such as task completion, answer quality, tool success, retrieval relevance, cost, and drift—chosen to match the product’s promises.
Why latency and error rate are not enough
A request can return HTTP 200 quickly and still fail the user: an agent may choose the wrong tool, retrieve irrelevant material, produce an unsupported answer, or stop before the task is complete. Conversely, a quality problem may develop gradually without causing an outage or a spike in server errors.
Microsoft Learn’s guidance on observability for generative AI and agentic systems states that “Uptime and error rates are not good indicators of quality and reliability in AI systems.” That does not make conventional service-level indicators (SLIs) obsolete. Latency, traffic, errors, and saturation remain necessary for service health; they simply cannot stand in for AI feature quality. Google Cloud’s reliability guidance likewise treats task success, response quality, safety, retrieval, token use, and drift as relevant dimensions.
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Start with the user or business promise, then define the population, numerator, denominator, evaluation method, and time window. The following are candidate specifications, not universal targets. Use only the signals relevant to the feature and make the measurement limitations explicit.
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| Dimension | Example SLI | How to measure it responsibly |
|---|---|---|
| Task completion | Eligible requests that complete the intended workflow successfully ÷ eligible requests | Define success as an application outcome, not a successful model call or HTTP response. Use outcome events or human-reviewed evaluation. |
| Answer quality | Evaluated responses meeting a declared correctness, relevance, or groundedness rubric ÷ evaluated responses | Version the rubric and evaluation set. Where appropriate, combine automated evaluation with human review or evidence of user outcomes. |
| Safety and policy | Eligible responses violating a specified safety or policy rule ÷ eligible responses | Record both the policy decision and enforcement outcome. A classifier or judge is a measurement proxy, not ground truth. |
| Tool execution | Valid successful tool calls ÷ eligible tool calls, or completed tool-dependent tasks ÷ eligible tasks | Track tool identity, result, errors, retries, permissions, and per-step latency. Record arguments and result details only as permitted by privacy and access rules. |
| Retrieval quality | Evaluated requests with relevant evidence and appropriately grounded output ÷ evaluated retrieval requests | Retain retrieval provenance and define relevance and attribution criteria for the particular use case. |
| Cost and efficiency | Tokens or measured inference spend per successful task, or tasks within a cost budget ÷ eligible tasks | Attribute consumption to a run or task, including retries and tool loops when measurable; aggregate model-call counts alone can miss expensive workflows. |
| Drift and stability | Quality or outcome change against a versioned baseline; or compliance with data-freshness and drift thresholds | Compare relevant model, prompt, data, cohort, and workflow versions. Define thresholds and review them as the system changes. |
For every ratio, state which requests qualify, what counts as success or failure, how cases are sampled or evaluated, and who owns the signal. An unreviewed model-judge score should not be the sole correctness SLI: its score depends on the evaluator, rubric, sampling, and available ground truth.
Keep service-health indicators alongside quality indicators
Continue to monitor latency, traffic, errors, and saturation across the application and its dependencies. For AI features, distinguish end-to-end latency from per-step latency and consider time to first token (TTFT) when users receive streaming responses. Track model-specific failures, tool-call volume and failure rates, throughput, resource saturation, and token consumption or measured inference spend where those affect reliability or the user promise.
These signals answer different questions. A service-health SLI can show that requests are failing or slowing down; trace and cost signals can help locate a slow or expensive step; outcome evaluation can show whether completed work meets the required standard. None of those measurements alone explains the entire experience.
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An agent run may involve an initial request, several model interactions, retrieval, tool calls, retries, and a final response. Capture enough connected event detail to reconstruct that path, rather than treating the final model call as the whole request.
- Correlate steps with traces, including the end-to-end run and the duration and outcome of individual model, retrieval, and tool operations.
- Record the model and relevant versions, token use, tool identity and outcome, retries, and retrieval provenance. Use OpenTelemetry’s GenAI semantic conventions where they fit your telemetry so common event concepts can be represented consistently.
- Connect traces to application outcome events and evaluation results so a team can investigate not only what ran, but whether the task met its defined outcome.
- Keep detailed prompts, responses, retrieved content, and tool arguments out of broad metric labels. Apply data minimization, access controls, retention limits, encryption, residency requirements, and applicable compliance rules to any stored payloads.
Tracing execution does not require indiscriminate storage of private reasoning text. Capture operational behavior and the evidence needed to debug or evaluate the feature under explicit governance rules.
Turn a user promise into an SLI and SLO
Google SRE distinguishes the SLI specification—the outcome that matters—from the implementation used to measure it. For example, “eligible agent tasks complete successfully” is a specification; application outcome events, server metrics, synthetic probes, or client-side instrumentation are possible implementations. They can measure the same promise with different fidelity, coverage, diagnostic value, and cost.
- State the promise. Define the user-visible outcome, such as successful completion of an eligible task, a response grounded in relevant evidence, or a harmful-output rate below a risk-appropriate limit.
- Define the SLI. Specify eligible requests, numerator and denominator, evaluation rubric, sampling method, aggregation window, and owner. Separate quality criteria from transport success.
- Choose the measurement. Use outcome events, trace-derived measures, evaluation pipelines, or other implementations appropriate to the promise. Check whether the method covers the real user experience and can diagnose failures.
- Set an SLO from needs and evidence. Choose a target based on user needs, risk, and the measured baseline—not on an example borrowed from another product. Review whether the target and evaluation method remain meaningful as models and workflows change.
- Use the result operationally. Assign an owner and a response when the SLO is missed, such as investigating a workflow regression, reviewing a policy failure, or addressing a drift threshold breach.
Google Cloud’s AI and ML reliability guidance, last updated August 7, 2025, gives illustrative examples: 99.9% of API calls returning a successful response, 95th-percentile inference latency below 300 ms, TTFT below 500 ms for 99% of requests, and harmful output below 0.1%. These are examples from that guidance, not generally valid targets. Product needs, risk, baseline performance, and the quality of the measurement determine appropriate goals.
Choose telemetry and evaluation that teams can govern
When selecting an observability design or platform, assess whether it can measure the outcomes that matter for your feature, reconstruct multi-step runs, support repeatable versioned evaluations, and connect production behavior to those evaluations. Also consider control over payload capture, access, retention, and data residency; interoperability with existing telemetry and OpenTelemetry GenAI conventions; and the operational cost and diagnostic usefulness of the signals.
There is a practical trade-off: user-proximate measurements may reflect actual experience more faithfully, while server-side measurements can offer better detail for diagnosing service behavior. A layered approach often uses request-level SLOs for service health, trace-derived measures for model and tool execution, and continuously evaluated outcome signals for quality and safety. The right design depends on the promise being measured and on what data can responsibly be collected.
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