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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Latency and error rates tell you whether an AI service responded; they do not tell you whether it solved the user’s problem. For a production LLM feature or agent, keep the usual service-health indicators and add outcome measures for task completion, answer quality, safety, tool execution, retrieval, cost, and drift. Define each measure against a real user promise, with an explicit population, denominator, evaluation method, and known limits.
Why latency and error rate are not enough
A request can return quickly with HTTP success and still produce a fabricated answer, fail to complete a workflow, call the wrong tool, or return irrelevant evidence. Conversely, an unsuccessful model call may be retried and ultimately lead to a successful task. Request-level availability and latency remain necessary signals, but they are not substitutes for measuring the behavior and outcome of the AI feature.
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Microsoft Learn’s guidance on generative and agentic AI observability says that uptime and error rates are not good indicators of AI quality and reliability. Google Cloud’s AI/ML reliability guidance likewise calls out task success, harmful or irrelevant responses, retrieval quality, token use, and drift alongside conventional service indicators.
Start with the product promise, such as “an eligible request completes the intended workflow safely and with relevant evidence.” Then choose indicators that show whether that promise is being kept. A metric is not useful merely because a system can emit it.
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Which AI-native SLIs are worth measuring?
The following are candidate specifications, not universal targets. Select only the ones relevant to your feature and risk profile. For every rate, define eligibility and the denominator before collecting a score.
| Dimension | Example SLI | What to instrument and define |
|---|---|---|
| Task completion | Eligible requests completing the intended workflow successfully ÷ eligible requests | Record an application-level completion outcome. Define what “complete” means independently of a successful HTTP response; consider human-reviewed evaluation where an outcome event is insufficient. |
| Answer quality | Evaluated responses meeting a stated correctness, relevance, or groundedness rubric ÷ evaluated responses | Version the rubric and evaluation set. State whether scores come from human review, automated evaluation, or outcome evidence, and do not treat an unvalidated model-judge score as ground truth. |
| Safety and policy | Eligible responses violating a specified rule ÷ eligible responses | Capture the policy decision and whether enforcement occurred. Specify the rule and denominator; a classifier or policy label is a measurement proxy, not automatically the true rate of harmful output. |
| Tool execution | Valid successful tool calls ÷ eligible tool calls, or completed tool-dependent tasks ÷ eligible tasks | Record tool identity, outcome, retries, permissions, result-schema validity, and per-step latency. Choose calls or tasks as the unit based on the promise being measured. |
| Retrieval quality | Evaluated retrieval requests with relevant evidence and grounded output ÷ evaluated retrieval requests | Retain retrieval provenance where permitted. Define what counts as relevant evidence and adequate attribution for the particular use case. |
| Cost and efficiency | Measured tokens or inference spend per successful task; alternatively, tasks within a defined cost budget ÷ eligible tasks | Attribute usage to runs and tasks, including retries and tool loops when measurable, rather than relying only on aggregate model-call totals. |
| Drift and stability | Outcome quality relative to a versioned baseline, or compliance with a defined data-freshness or drift threshold | Set baselines and review thresholds. Slice by model, prompt, data, cohort, or workflow version where those distinctions help explain a change. |
Google Cloud’s agent-observability guidance and Microsoft Learn’s AI observability guidance describe monitoring model interactions, tools, quality, safety, and execution context. Google Cloud’s AI/ML reliability guidance also covers user-aligned task outcomes, retrieval, resource use, and drift. These sources recommend dimensions and methods; they do not establish one universal rubric or evaluator as ground truth.
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How should an SLI be specified?
An SLI specification describes the user-relevant outcome; its implementation is the way the team measures it. Google SRE’s “Implementing SLOs” makes this distinction and notes that quality, correctness, freshness, durability, and coverage can be SLO dimensions alongside availability and latency. Google Cloud’s “SLI metrics overview” discusses comparing implementations by fidelity, coverage, and cost.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Write down these elements before setting a target:
- Promise and eligible population: Say which requests count, including exclusions such as unsupported intents or test traffic.
- Numerator and denominator: Make success and failure explicit. For example, a tool-call success rate needs a clear definition of an eligible call and a valid successful result.
- Evaluation protocol: Record the rubric or rule, evaluator type, sampling approach, and version. If human review is used, define how reviewed examples represent production traffic.
- Window and owner: Choose an aggregation window appropriate to the product and assign a team that can investigate and act on breaches.
- Measurement limits: State what the signal cannot establish. A safety classifier’s output, for instance, is not by itself proof of the actual rate of policy violations.
Compare measurement options against the same promise. Client-side events can be closer to the user’s experience; server-side events may provide better diagnostic context. Synthetic checks can cover selected scenarios but may not represent the eligible production population. A layered design often combines request-level service SLOs, trace-derived execution measures, and continuously evaluated outcome signals.
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What should an AI-agent trace capture?
Treat an agent as a multi-step execution path, not a single model call. Correlate the request, model interactions, retrieval, tools, retries, and final outcome so an operator can locate where a run went wrong.
- Record per-step and end-to-end latency, model or operation identity, token usage where available, and model-specific failure categories.
- For tools, capture tool identity, call outcome, retry count, permission or validation result, and result-schema status. Store arguments and returned content only when justified and permitted.
- For retrieval, retain source provenance and relevant retrieval metadata so reviewers can understand what evidence informed an answer.
- Connect run-level execution events to the application’s final task outcome and, where available, the quality or safety evaluation.
- Use OpenTelemetry’s Generative AI semantic conventions as a reference for interoperable GenAI telemetry fields, while keeping sensitive content out of broad metric labels.
Google Cloud’s “Agent observability” and Microsoft Learn’s “Observability for Generative AI and agentic AI systems” discuss logs, metrics, traces, model interactions, tools, retrieval context, and evaluation. The aim is enough event detail to diagnose execution and outcomes—not indiscriminate storage of private reasoning text.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you keep telemetry useful and safe?
Prompts, responses, retrieved documents, and tool arguments may contain sensitive or regulated information. Decide what to collect, where to retain it, who may access it, and for how long before enabling payload capture. Apply data minimization, access controls, retention rules, encryption, and applicable residency and compliance requirements.
Keep high-cardinality or sensitive prompt and response payloads out of general-purpose metric labels. Prefer bounded dimensions for metrics and access-controlled traces or evaluation stores for details that are necessary for debugging. A trace should have a clear operational purpose, an owner, and a retention policy.
Quality and safety measures also need governance. Version evaluation rubrics and datasets, monitor whether sampled cases still represent production traffic, and review automated evaluators against human judgment or outcome evidence. A changing prompt, model, data source, or workflow can invalidate a baseline even if the dashboard still emits a number.
How should teams set targets?
Set SLO targets from user needs, risk, and a measured baseline—not from an example in a vendor guide. Google Cloud’s AI/ML reliability guidance, last updated 2025-08-07 UTC, gives illustrative examples: 99.9% of API calls returning a successful response; 95th-percentile inference latency below 300 ms; time to first token (TTFT) below 500 ms for 99% of requests; and harmful output below 0.1%. Those are published examples, not generally valid targets for every application.
For an outcome SLO, first confirm that the indicator has a defensible evaluation method and sufficient coverage. A target for an unrepresentative sample or an unstable rubric can create false confidence. Keep service-health objectives and AI-outcome objectives distinct enough that a healthy API cannot mask poor task completion or unsafe behavior.
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How can you tell whether an indicator helps?
Review each proposed signal against four practical questions:
- Does it reflect the promise? A measure should correspond to an outcome users or the business care about, not simply an event that is easy to count.
- Does it cover the relevant path? Check whether the measure includes model steps, retrieval, tools, retries, and eligible user cohorts as appropriate.
- Can it explain a change? Correlation with traces, versions, and outcome events should help an owner identify likely causes and take action.
- Is the collection cost justified? Balance fidelity and diagnostic value against engineering effort, evaluation cost, telemetry volume, and the privacy implications of retained data.
These checks help distinguish a useful SLI from a dashboard metric that has no clear decision attached to it. Review the signal when product behavior, model versions, workflow steps, or evaluation methods change.
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