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Start with error-budget burn rate rather than a single latency or error percentage. For most AI-agent services, page when the measured burn rate is about 10× the baseline over a 1–2 hour window, and create a lower-severity alert at about 2× over 24 hours. Add separate alerts for model-call and tool-call failures, then layer in task success, latency, time to first token, safety, quality and cost signals. These are starting points, not universal limits: tune them to traffic volume, workflow complexity and user impact.
The recommended starting policy
An SLO defines the proportion of good events you promise during a compliance period. The error budget is the remaining proportion of events that may fail. A 99.9% SLO therefore has a 0.1% error budget. Burn rate expresses how quickly the current failure rate would spend that budget.
Use two burn-rate policies against your primary, user-facing SLO:
| Policy | Starting threshold | Lookback | Typical action | Why it exists |
|---|---|---|---|---|
| Fast burn | 10× baseline | 1–2 hours | Page the on-call engineer | Finds severe incidents before they consume a large share of the budget |
| Slow burn | 2× baseline | 24 hours | Open a ticket or page during business hours | Catches sustained degradation that a short window may miss |
Google Cloud describes 10× over a one- or two-hour lookback and 2× over 24 hours as useful starting policies. Its agent alert-configuration pattern uses a one-hour fast-burn window and a three-day slow-burn window, plus independent model and tool error alerts. Treat the three-day pattern as an implementation example rather than a constant that every service should copy.
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The alert should represent user-visible reliability. If an agent can return HTTP 200 while failing to complete the requested task, an API availability alert alone will remain green during a real outage. Define the primary SLI around successful task completion or another outcome that users recognize as success.
How error budgets and burn rate work
Error-budget formula
The basic relationship is error budget = 1 − SLO goal. For a 99.9% objective, the budget is 0.001 of all eligible events, or 0.1%.
Burn-rate formula
Burn rate is the observed bad-event fraction divided by the allowed bad-event fraction:
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A burn rate of 1 means the service is spending its entire budget at exactly the rate that would exhaust it at the end of the compliance period. A value above 1 predicts exhaustion sooner; a value below 1 predicts that some budget will remain.
What the numbers mean for a 99.9% SLO
Google SRE’s 2018 example uses a 30-day compliance period and a 99.9% SLO. The resulting relationships are:
| Burn rate | Observed error rate | Time to spend the full budget if sustained |
|---|---|---|
| 1 | 0.1% | 30 days |
| 2 | 0.2% | 15 days |
| 10 | 1% | 3 days |
| 1,000 | 100% | About 43 minutes |
These are mathematical consequences of that specific 99.9%/30-day example, not promises about an AI system. A 10× alert is intentionally urgent because it detects a rate that would consume the monthly budget in three days, while a one-hour window limits how long the team waits to confirm the problem.
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Alerting on a fixed share of the budget
You can also choose an alert by the fraction of budget spent during a window. In the same 30-day example, Google SRE calculates that consuming 5% of the budget in one hour corresponds to a burn rate of 36. This approach is useful when the team has agreed that a particular budget loss should trigger intervention, but the resulting threshold must be recalculated whenever the compliance period or window changes.
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Choose SLIs that describe an agent’s real service
An AI agent is a chain of model calls, retrieval steps, tool invocations and policy checks. Monitor the user outcome and the components that explain it.
Primary task-success SLI
Count an event as good only when the requested workflow reaches its defined success state. Examples include a support case being correctly classified and updated, a deployment change being applied and verified, or a research answer passing required factual and policy checks. Document exclusions such as user cancellation, invalid authentication or an intentionally abandoned workflow; otherwise the denominator will move when traffic mix changes.
Request and workflow latency
Use a percentile and threshold tied to the workflow. A single average hides the long tail created by retries and multi-step tools. Track end-to-end completion latency separately from individual model-call latency so that a fast model cannot mask a slow tool chain.
Time to first token
TTFT measures how long a user waits before streaming begins. It is useful for conversational agents, but it does not prove that the eventual answer is correct or that the task completed. Keep TTFT as a separate latency SLI.
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Model-call and tool-call errors
Emit separate counters for provider timeouts, rate limits, malformed responses, tool authentication failures, tool timeouts and downstream 5xx responses. Alerting on these dimensions lets responders identify the dependency without changing the top-level task SLO.
Quality and safety
When the product requires it, measure response-quality failures, incorrect tool selection or arguments, hallucination findings, harmful-output rate and safety-policy triggers. A quality or safety SLO can be evaluated from sampled human review, automated evaluators or policy classifiers; record the evaluation method and sampling rate so a change in measurement is not mistaken for a product regression.
Cost as a supporting signal
Track tokens per task, spend per successful task and sudden changes in model or tool usage. Cost alerts should remain distinct from the reliability SLO: a cheap but failing agent is not healthy, and an expensive but reliable workflow may require product or capacity action rather than an incident page.
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Set thresholds step by step
- Choose the compliance period. Thirty days is common for a monthly reliability objective, but select a period that matches release and reporting practices. Write the period into the SLO definition because all burn-rate calculations depend on it.
- Define the denominator and good event. Specify which agent requests count, what constitutes successful completion, and which exclusions are legitimate. Validate the SLI against real user journeys before creating alerts.
- Establish a production baseline. Measure normal error, latency, TTFT, quality and safety distributions across representative traffic. Separate scheduled jobs, interactive sessions and high-risk workflows when their behavior differs materially.
- Create the fast-burn page. Start near 10× burn rate with a 1–2 hour lookback. Google Cloud’s agent example uses one hour. Require the condition to persist for a short confirmation interval appropriate to your sampling rate; do not use a confirmation delay longer than the time in which users would be materially affected.
- Create the slow-burn policy. Start near 2× over 24 hours for a ticket or lower-severity page. A three-day window is another documented pattern for agent deployments. Pick one window deliberately: 24 hours finds a problem sooner, while three days smooths intermittent traffic and is less sensitive to short-lived spikes.
- Add component alerts. Alert separately on model-call and tool-call error rates, with ownership routed to the team that can remediate each dependency. Keep these alerts linked to the top-level incident so responders can see whether component failures explain lost task success.
- Add quality, safety and cost policies. Set thresholds only where the product has a defined tolerance and a response playbook. A harmful-output alert, for example, should identify the policy category, affected model or workflow and required containment action.
- Route by severity. Page for fast burn or an immediate safety risk; create tickets for slow burn and cost anomalies unless they are causing user-visible failure. Include the SLO, current burn rate, window, affected workflow and representative traces in the notification.
- Test with historical data. Replay known incidents and quiet periods. Check whether the policy would have paged quickly during a real failure and stayed quiet during normal low-volume periods. Adjust the window or threshold, not the SLO definition, merely to reduce an inconvenient page.
Fast-burn versus slow-burn alerts
| Characteristic | Fast burn | Slow burn |
|---|---|---|
| Detection speed | Minutes to roughly two hours | Hours to several days, depending on the window |
| Starting threshold | About 10× baseline | About 2× baseline |
| Budget consumed before notification | Higher rate, but short observation time | Lower rate, accumulated over a longer period |
| Low-traffic sensitivity | Can be noisy when only a few events occur | Usually more stable because it aggregates more events |
| Operational severity | Page and investigate immediately | Ticket, planned remediation or lower-severity page |
| Diagnostic value | Shows an active incident | Shows a trend that may require capacity, prompt or dependency work |
Do not interpret these policies as competing definitions of failure. They are different observation lenses on the same SLO. A service may trigger both when a severe incident persists; deduplicate notifications while retaining the first fast-burn page as the incident start.
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Latency and task-success thresholds for AI agents
Google Cloud gives illustrative AI/ML targets of 99.9% successful API calls, p95 inference latency below 300 milliseconds, and TTFT below 500 milliseconds for 99% of requests. It also gives a harmful-output rate below 0.1% as an example safety objective. Those figures are examples, not universal agent limits. A multi-step agent that waits on retrieval and external systems cannot be judged by a single-model inference target.
Use separate objectives for different user promises
- Reliability: percentage of eligible tasks that complete successfully.
- Responsiveness: p95 or p99 end-to-end latency, plus TTFT where streaming matters.
- Correctness and tool use: evaluated success rate, valid tool arguments and verified outcomes.
- Safety: harmful-output and policy-violation rates.
- Efficiency: tokens or money per successful task, monitored separately from the reliability budget.
Each objective needs its own denominator and evaluation method. Do not combine latency, safety and correctness into one opaque score; a responder must know which promise is failing and what action can restore it.
Adjust for traffic, sampling and compliance period
Low traffic
Short windows become unstable when the denominator is small. One failed task out of ten can appear as a 10% error rate even though the estimate is statistically weak. Use a longer lookback, a higher minimum-event requirement, or both. A threshold should not page on a single event unless that event represents an immediate safety or security emergency.
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Align windows with the traffic pattern. For a batch agent that runs once per day, a one-hour burn window may contain no events and provide no useful signal. Evaluate each batch as an eligible event, use a longer window, and add a deadline alert for missed runs.
Changing compliance periods
Burn rate is tied to the SLO period. If you move from a 30-day to a seven-day objective, recalculate every threshold and budget-spend interpretation. Never carry a numeric burn threshold across periods without checking the implied time to exhaustion.
Changing workflows
Revisit the SLO when users, prompts, tools or model routing change. Google Cloud guidance recommends reviewing SLOs every six to twelve months. A review should confirm that the SLI still represents user value, the target still matches expectations and the alert produces an actionable response.
Quick Recap
Diagnose an alert without losing the user outcome
- Confirm the alert window, burn rate and affected SLO; check whether the event count is large enough to trust.
- Compare task-success failures with model-call and tool-call error series to identify the failing stage.
- Inspect latency and TTFT separately. A latency spike with normal task success may require capacity work, while task failures with normal latency often indicate bad outputs, tool arguments or policy rejection.
- Check recent model, prompt, tool, routing and configuration changes, including quota and authentication changes.
- Contain user harm first: disable a failing tool, route to a known-good model, limit risky actions or require human approval where the runbook allows it.
- After recovery, calculate budget consumed, document the cause and decide whether the SLO, alert window or implementation needs a justified change.
Common threshold mistakes
- Paging on raw error percentage: a fixed 1% threshold means different things for a 99% and a 99.9% SLO. Burn rate keeps the alert tied to the promised reliability.
- Using only infrastructure health: healthy servers and HTTP responses do not prove that an agent completed the user’s task.
- Copying 10×, 2×, 300 ms or 500 ms unchanged: each is a documented starting point or example, not a product-independent limit.
- Ignoring denominator quality: retries, duplicate requests and excluded failures can make the SLI look better without improving the experience.
- Combining all failures into one page: responders need model, tool, quality and safety dimensions to choose the right mitigation.
- Alerting on cost as reliability: cost anomalies need a financial or capacity response, while reliability incidents need user-impact remediation.
- Never reviewing policies: an alert that pages constantly is ignored; one that never fires creates false confidence.
A practical rollout checklist
- Document the task-success event, exclusions and compliance period.
- Calculate the error budget as
1 − SLO. - Implement burn-rate recording for at least one representative production traffic segment.
- Configure a fast-burn page near 10× for 1–2 hours.
- Configure a slow-burn ticket or page near 2× for 24 hours, or use a documented three-day pattern when traffic requires more smoothing.
- Add independent model-call and tool-call error alerts.
- Define latency and TTFT percentiles that match the workflow, rather than copying single-model benchmarks.
- Set explicit quality, harmful-output and policy-trigger objectives where the product requires them.
- Monitor token and cost anomalies without folding them into the reliability SLO.
- Test against historical incidents, then review the policies every six to twelve months or after major workflow changes.
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