Grafana Applies SLOs and Error Budgets to AI Agent Behaviour

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…