SLOs give AI agent errors a measurable budget
SLOs, or service level objectives, are agreed targets for how well a system should behave, and Grafana Labs argues they can be applied to AI agents in the same way they have long been used for uptime and reliability.
The practical problem is that normal observability signals do not answer the most important question about an AI agent: whether it is useful and safe. Latency, meaning how long a reply takes, can look good. Token counts, which measure how much text an AI model processes, can stay low. Error rates and traces, the step-by-step records of tool calls, can all look clean. Yet the agent can still give an answer that is fluent, confident and wrong.
Grafana Labs describes that gap as a reliability blind spot. If teams cannot measure agent quality, every prompt change, model change or release becomes a guess. The proposed answer is to use evaluations, which are checks that score real conversations, single messages or individual tool calls. A judge, often another language model, reviews the output and returns either a pass or fail result, or a rating on a scale.
Those evaluations can ask specific questions. Was the answer grounded in the context provided, meaning did it stick to the information it was given? Did it fulfil the user’s request? Was it toxic? Did it expose personal information? Did it fall for a prompt injection, a trick that tries to make the agent ignore its instructions? In some cases, no AI judge is needed: a regular expression, a simple text-matching rule, can detect whether a response contains something like an API key, which is a secret code used by software to access a service, or whether it names a competitor.
Grafana Labs says Grafana Cloud’s Agent Observability can use both deterministic checks, where the same input should always produce the same verdict, and model-based judges. The more general lesson is not tied to one tool: agent quality is not one number. An agent may be grounded but slow, helpful but occasionally toxic, or correct but too expensive to run. For that reason, Grafana Labs recommends measuring separate behaviours such as groundedness, fulfilment, toxicity, latency and cost, rather than hiding them inside a blended accuracy score.
The scoring method can be simple or more detailed. A pass or fail check may be enough for some behaviours. For others, a scale can be more useful. Grafana Labs gives the example of scoring groundedness from one to five, then treating scores of four and above as a pass. That creates a rate: the share of conversations that cleared the chosen bar. Its dashboards can show executions, pass rate, failures, evaluators, judge cost and pass rate by evaluator. Its Assistant can also create an evaluation for a chosen behaviour and then create and enable evaluation rules specific to an agent.
A number on a dashboard is still not a decision. That is where an SLO changes the operational model. Grafana Labs gives the example of measuring the fraction of conversations a judge marks as fulfilled and setting a target of 95% fulfilled over 30 days. The remaining 5% becomes the error budget, meaning the amount of unreliability the team has decided it can tolerate during that period.
That framing matters because it changes how teams act. If the budget is healthy, a team can test an ambitious prompt or a new model with some room for failure. If the budget is nearly spent, the signal is to slow feature work and improve quality. Grafana Labs also notes that a change can be replayed against known conversations before release, so a prompt or model that breaks a measured behaviour is caught before it reaches users.
The approach is especially relevant because agent failures are not always dramatic. A short spike in bad conversations may be visible and may barely affect a 30-day budget. The more dangerous pattern is a slow burn: a small, steady decline where no single answer looks alarming, but quality drains away over time. Grafana Labs lists possible causes including a prompt that has gone stale, a model upgrade that is subtly worse for the use case, or users gradually asking different kinds of questions. Because agent answers can remain plausible while drifting, simple thresholds may miss the problem. A budget measures the accumulation.
Choosing the first target is the hard part. Grafana Labs says teams do not need to invent it immediately. Once evaluations have run long enough to build a useful sample, Assistant can recommend SLO targets based on observed scores in the customer’s Grafana Cloud environment, and can create SLOs from those measurements. Teams can then tighten or loosen the target. Grafana Labs contrasts a young agent entering a new area, which may accept a coin-flip chance of missing its target, with an agent facing paying customers, which may want only a 5% chance of a slip.
For IT and business teams, the transferable point is clear: treat AI agent behaviour as an operational reliability issue, not just a model-quality discussion. Pick the behaviours that matter, measure them separately, set explicit targets, and use the error budget as a shared language between product teams and on-call engineers. For organisations applying similar discipline across infrastructure, AEU-I is relevant as security-first IT, infrastructure and consulting.
The lesson is not that every agent needs the same target, judge or dashboard. It is that vague confidence is a poor substitute for measured behaviour. Evaluations make agent output visible, SLOs turn those measurements into commitments, and error budgets help teams decide when to experiment and when to stop and fix quality.