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Set separate objectives for success, latency, safety, cost, and dependencies; define exactly which events consume each budget; then connect budget burn to release, retry, and autonomy decisions. There is no universal target for every agent. A low-risk drafting assistant and an agent that changes production systems require different reliability and approval thresholds.
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What an SLO means for an AI agent
Google defines a service-level objective (SLO) as a target value or range for a service level measured by a service-level indicator (SLI). For an agent, the SLI must represent the user journey rather than merely whether an HTTP request returned 200. A useful question is: did the agent complete the requested outcome, within the promised time, at an acceptable cost, without crossing a safety or authorization boundary?
An error budget is the amount of failure an objective permits. Google SRE states that “a budget is simply 1 minus a service’s SLO.” If the objective is 99.9% successful runs, the budget is 0.1%; at 99.99%, it is 0.01%. A 99.99% availability objective therefore allows 0.01% unavailability, not zero downtime. Google also notes that insisting on 100% compliance is both unrealistic and undesirable because it can suppress useful changes or impose disproportionate cost.
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| SLO | Error budget | Illustrative allowance in 100,000 runs |
|---|---|---|
| 99.9% | 0.1% | 100 misses |
| 99.99% | 0.01% | 10 misses |
| 99.999% | 0.001% | 1 miss |
The last column is simple arithmetic, not a recommendation. State the measurement window (such as a rolling 28 days), denominator, exclusions, and response policy alongside every target. A request-based SLO and a task-run SLO can produce very different results when one run makes many model and tool calls.
Which indicators belong in an agent SLO
Use a small set of indicators tied to distinct user journeys. Keep materially different journeys separate: an informational assistant, a batch research worker, and a production-change agent have different consequences and timing expectations.
Task success and valid completion
Task success is the proportion of runs that achieve the requested outcome and pass an objective validation step. Google Cloud describes successful task completion as an AI reliability measure. Validation might check a schema, a reconciliation total, a test suite, or a human-approved acceptance criterion. Track a separate valid-response or availability rate for requests that return a usable result instead of a timeout, crashed run, tool error, malformed output, or policy block.
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Latency
Measure end-to-end completion time, not only model response time. Interactive agents should also track time to first token (TTFT). Google Cloud gives illustrative targets of 95th-percentile inference latency below 300 milliseconds and TTFT below 500 milliseconds for 99% of requests. These are examples, not defaults; a long-running workflow may have a minute-level completion objective while still needing a quick progress acknowledgement.
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Safety and quality
Track harmful-output rate, hallucination or unsupported-claim rate, policy-violation rate, unauthorized-action rate, and human-override rate. Google Cloud gives an illustrative harmful-output rate below 0.1%, while Microsoft’s agent-SRE reference includes hallucination rate. Treat severe safety events as hard limits or separate budgets rather than allowing a high volume of harmless successes to dilute them.
Cost and resource use
Measure cost per successful task, token consumption, tool-call count, and wall-clock duration. Microsoft’s reference explicitly models cost per task. A run that eventually succeeds after exhausting a token or tool budget is not economically reliable, even if its final text is correct.
Dependency health
Record timeout and error rates for model providers, retrieval stores, queues, APIs, and every tool. Dependency SLIs explain why the agent missed its objective and show whether a retry policy is masking a deteriorating upstream service.
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Count an event against the relevant budget when it violates the promised user outcome or a safety boundary. Define the classification before collecting data so operators cannot quietly relabel inconvenient outcomes.
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- Availability failure: no response, timeout, crashed run, or exhausted turn limit.
- Correctness failure: an answer or action fails validation or does not complete the requested task.
- Tool or dependency failure: an API, retrieval system, queue, model call, or other dependency fails and the run cannot recover within policy.
- Safety or authorization failure: harmful output, policy violation, data leak, unauthorized tool call, or an action outside the agent’s scope.
- Freshness or grounding failure: stale or unsupported information is used when the task requires current, sourced state.
- Economic failure: the run exceeds its cost, token, duration, or tool-call budget.
Write explicit exclusions for synthetic tests, out-of-scope tenants, planned maintenance, and invalid requests. Audit those exclusions: Google’s SLO guidance warns that miscategorized errors can distort budget consumption and hide customer harm. If a user-facing request is invalid but the classifier cannot reliably identify that fact, count it conservatively until the classification is improved.
How to set an error budget policy
Choose a window and make the budget an operating policy, not a dashboard decoration. For a 99.9% task-success SLO over a rolling 28-day window, the budget is 0.1% of eligible runs in that window. Define whether a safety violation consumes only a safety budget or also the task-success budget; for high-impact actions, consuming both is usually clearer.
Use burn rate as well as remaining budget
Cumulative consumption shows how much allowance remains. Burn rate shows how quickly it is being spent relative to the planned window. A service can have half its budget left but still be on course to miss the objective if failures accelerate. Alert on fast burn and on projected exhaustion, not only on a zero balance.
Connect budget states to decisions
| Budget state | Operating response |
|---|---|
| Healthy | Ship changes through normal review, keep standard monitoring, and continue improving tests. |
| Burning quickly | Slow the rollout, increase sampling and human review, and investigate the dominant failure class. |
| Exhausted | Freeze non-urgent releases and prioritize reliability work until the service is back within policy. Urgent security and bug fixes may proceed under explicit approval. |
| Large incident | Require a postmortem and corrective action. Google’s example uses consumption of more than 20% of a four-week budget as a trigger for this response. |
Review targets when user expectations, workload, dependency behavior, or risk changes. A target that was appropriate for a pilot may be too loose for a widely deployed workflow, while an overly strict target can encourage expensive, low-value engineering.
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When an agent should retry a failed tool call
Retries are recovery mechanisms with a ceiling, not a substitute for correctness. Google warns that retries can amplify low error rates into higher traffic and cascading failure.
Retry only bounded, transient failures
- Retry timeouts, temporary network faults, rate-limit responses such as 429, and selected 5xx responses when the dependency documents them as transient.
- Use exponential backoff with jitter so many agents do not retry simultaneously.
- Set both a maximum attempt count and a maximum elapsed time for each operation and for the whole run.
- Give each dependency or operation its own retry budget so one flaky service cannot consume the entire workflow budget.
Stop immediately on permanent or unsafe failures
- Do not retry authentication, authorization, policy, schema-validation, malformed-input, or deterministic business-rule errors.
- Do not repeat a side effect unless the operation is idempotent or carries an idempotency key and the receiver can deduplicate it.
- Stop when the remaining deadline cannot accommodate another attempt or when validation shows that the requested action is unsafe.
Protect the system while retrying
Open a circuit breaker when failure rate or latency crosses a defined threshold; fail fast or route to a safe fallback while the dependency recovers. Record every attempt, reason, delay, dependency, idempotency key, and final outcome in a trace. Durable orchestration, such as the run handling documented by the OpenAI Agents SDK, can preserve state across waits, retries, or process restarts and can report a run that exceeds its maximum turns. Durable state prevents a restart from duplicating side effects, but it does not make an unsafe retry safe.
Acceptable risk and autonomy tiers
Availability targets express only part of risk. Add consequence, reversibility, data sensitivity, and the possibility of physical or financial harm. Google’s AI-operations guidance calls for “No Ambient Access & Least Privilege,” progressive authorization, agent-specific circuit breakers, mandatory dry-run support, and zero-trust, safe-by-default actuation. Separate the model’s reasoning engine from a deterministic execution engine so a prompt or model update cannot bypass production controls.
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| Tier | Typical work | Required controls |
|---|---|---|
| 0: Informational | Drafts, explanations, or answers with no external side effect. | Quality and safety checks; automated execution is acceptable when outputs remain advisory. |
| 1: Reversible action | Create a ticket, schedule a job, or edit a draft. | Scoped credentials, input and output validation, audit logging, and an easy undo path. |
| 2: Consequential action | Change customer data, access rights, money, or production state. | Dry run, deterministic policy checks, rate limits, narrow permissions, and human approval or two-party control. |
| 3: Safety-critical or irreversible | Actions where an error cannot be readily undone or could cause serious harm. | Human control by default, deterministic tooling, fail-closed behavior; the language model proposes rather than directly actuates. |
Implementation checklist for a production agent
- Map journeys and consequences. List each user-visible workflow, its success condition, data sensitivity, reversibility, and risk tier.
- Define objectives. Choose SLIs, target values, measurement windows, exclusions, and data-quality checks for success, latency, safety, cost, and dependencies.
- Instrument complete traces. Capture model and prompt version, tool and dependency, retries, token and monetary cost, policy decisions, approvals, and final outcome.
- Specify retry behavior. Classify transient versus permanent errors, set idempotency requirements, backoff, attempt and elapsed-time caps, and circuit-breaker thresholds.
- Build burn-rate dashboards. Display task success, latency percentiles, safety events, cost per successful task, dependency health, retry amplification, and remaining and projected budget.
- Start with constrained operation. Use shadow mode or human approval. Inject timeouts, malformed tool output, stale retrieval, prompt injection, dependency outages, and duplicate-execution scenarios.
- Increase autonomy gradually. Promote a workflow only after it meets its objectives and its control plane remains within budget under realistic load.
- Review monthly. Examine exclusions, incidents, near misses, and user impact; revise targets when workload or risk changes.
How to compare two agent designs
When choosing between architectures or vendors, compare the same workload and window. A design with a higher nominal availability can still be worse if it produces more unsafe actions or costs more per successful task.
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| Comparison axis | Question to answer |
|---|---|
| Task-success SLO | Which design completes and validates more requested outcomes? |
| Tail latency | What are p95 and p99 completion times and TTFT where interaction matters? |
| Safety and unauthorized-action rate | How often does each design violate policy, leak data, or exceed scope? |
| Cost per successful task | How much model, tool, and human-review spend buys one accepted result? |
| Retry amplification | How much extra traffic appears during a dependency incident? |
| Recovery time | How quickly can the system isolate a failing dependency and restore service? |
| Observability | Can operators reconstruct prompts, tool calls, approvals, retries, and outcomes? |
| Blast radius and reversibility | What is the largest possible impact of one bad run, and how quickly can it be undone? |
| Human approval burden | Which actions require review, and does the review queue itself become a bottleneck? |
Making autonomous operation safe
Keep the execution plane deterministic wherever consequences are high. The model may select from typed tools and propose parameters, but a policy engine should enforce identity, scope, freshness, rate, and approval rules before actuation. Give each agent only the credentials it needs for the current step; do not provide ambient access to production systems.
Use dry-run responses that show the exact proposed mutation, affected objects, estimated cost, and policy checks. Require explicit confirmation or two-person approval for consequential tiers. Apply rate limits and circuit breakers per agent, tenant, tool, and dependency. Fail closed when authorization, validation, or safety state is unknown. Keep an auditable record that links the user request, model version, retrieved evidence, tool inputs and outputs, approvals, retries, and final side effect.
An SLO becomes useful when it changes behavior: it tells the team what “good” means, how much imperfection is acceptable, when to stop retrying, and when autonomy must be reduced. Set the target from user impact and risk, measure the complete journey, and spend the error budget deliberately rather than treating it as permission to ignore harmful failures.
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