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An AI agent can return a successful response while failing the task: it might choose the wrong tool, ignore a constraint, or produce an answer that is unusable. Uptime measures whether a service is available; an agent service-level objective (SLO) should also measure whether the agent achieves the intended outcome safely and on time.
What uptime measures—and what it misses
Google SRE defines an SLO as “a target value or range of values for a service level that is measured by an SLI.” The service-level indicator (SLI) is the measurement; the SLO is the target applied to it. Common SLIs include availability, latency, error rate, and throughput. Google SRE’s SLO guidance explains the distinction.
- Availability: Could the user reach the service, or did the request return successfully?
- Task success: Did the agent complete the requested outcome to an accepted standard?
- Execution quality: Did it use suitable tools and follow the expected workflow and policy?
- User-relevant performance: Did it finish within an acceptable time and avoid harmful or irrelevant output?
These are related but distinct. Request success and uptime reveal operational health; they do not establish that the agent did the right thing. Google Cloud recommends connecting AI reliability measures to business KPIs, including request success, latency, harmful or irrelevant output, and successful completion of agent tasks (AI and ML reliability guidance).
What belongs in an agent SLO?
Build the SLO around the workflow and the consequences of failure. There is no universal threshold that fits every agent. Define the measured population and task, what counts as success, the measurement window, the latency boundary, exclusions, and how outcomes are verified. Segment by task type or risk when a single aggregate could hide important failures.
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Task completion
Count an outcome as successful only when it meets explicit task criteria. Verification might use ground truth, a task-specific automated check, human review, or another defined acceptance test. A returned answer—or a fluent one—is not enough by itself. Google Cloud’s guidance for production AI agents emphasizes measuring outcomes against the use case, rather than relying only on traditional language-model scores or simple feedback (production-agent KPI guidance).
Requests, dependencies, and latency
Track API or request success and failures in tools and other dependencies to see where execution breaks down. Measure end-to-end time to resolution, not only model response speed; time to first token can help assess perceived responsiveness, but does not show how long the full task took. The production-agent KPI guidance identifies end-to-end trace latency as especially important for agents.
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Quality, safety, and trajectory
Measure task-specific correctness and harmful or irrelevant output. For agents that can take action, also track policy violations, unsafe or unauthorized actions, and whether guardrails trigger when they should. Assess the trajectory as well as the final response: did the agent select an appropriate tool, provide valid arguments, and follow a suitable plan? These measures can expose a flawed process even when the final answer happens to look acceptable.
Cost per successful task
When cost matters, divide total operating cost by successful outcomes rather than looking only at cost per attempt or token use. Google Cloud illustrates the distinction with a run costing $0.10 that fails 50% of the time: its cost per successful result is twice the per-run cost. This is an explanatory example in its KPI article, not a measured industry benchmark.
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Google Cloud example targets are not agent defaults
Google Cloud’s AI/ML reliability documentation lists the following example SLO targets. They illustrate service-level measures; they are not prescribed agent task-success targets. The appropriate thresholds depend on the workflow and its users.
| Example indicator | Google Cloud example target |
|---|---|
| Successful API responses | 99.9% of API calls |
| Inference latency | 95th percentile below 300 ms |
| Time to first token | Below 500 ms for 99% of requests |
| Harmful output rate | Below 0.1% |
These are examples published in Google Cloud’s AI/ML reliability guidance, not universal recommendations. In particular, successful API responses cannot stand in for verified completion of a user’s task.
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Offline evaluations can test whether success criteria and verifiers reflect the real task; ongoing evaluation can reveal changes in production outcomes. Review final-response quality separately from tool use and other steps in the trajectory. Google Cloud’s Gen AI evaluation documentation describes both kinds of evaluation and labels the service Preview. Its trajectory exact-match metric checks whether tool calls match a reference in the same order; other supported metrics allow extra calls or compare ordering. This is one evaluation option, not the only way to assess an agent (Evaluate Gen AI agents).
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Check whether the provider SLA covers the agent path
An SLO is a measured objective; a service-level agreement (SLA) is a contractual commitment whose scope and exclusions depend on the particular terms. Check whether the provider’s commitment covers the complete path your users rely on, including the agent and its integrations. For example, Google’s current Gemini Enterprise SLA excludes specified requests originating from built-in agents, user-defined agents, and externally integrated agents. That is a reason to read the relevant contract closely, not evidence that every provider excludes agent requests.
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