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Grafana Labs is proposing a familiar reliability engineering approach for one of the less familiar problems in modern software: how to measure whether an AI agent is actually behaving well. In a recent article, the company describes using evaluations, service-level objectives (SLOs) and error budgets to turn behaviours such as hallucination, task fulfilment, toxicity, and cost into measurable targets that can be monitored over time. The idea addresses a fundamental limitation of conventional application observability. Traditional metrics can tell engineers that an agent responded quickly, encountered no technical errors, and consumed a certain number of tokens, but none necessarily indicate whether its answer was correct, grounded in available information, or useful to the user. Grafana's approach is to evaluate the behaviour itself and turn the result into an SLI that can be measured against an SLO.