What if your agent's hallucinations had a budget? How to start using SLOs for agent behavior

AI agent reliability cannot be judged solely through conventional operational metrics such as latency, token use, or error rates, because agents can produce fluent but incorrect responses. The proposed approach uses evaluations—either deterministic checks such as regexes or model-based judges—to measure distinct behaviors including groundedness, task fulfillment, toxicity, privacy leakage, prompt-injection resistance, latency, and cost. Each behavior can then be assigned a service level objective, such as 95% fulfillment over 30 days, with the remaining allowance treated as an error budget that supports controlled experimentation while signaling when quality needs attention. Tracking budget consumption helps distinguish isolated incidents from gradual quality drift, which may otherwise remain hidden in plausible-looking responses, and budget-based alerts can reduce noisy monitoring. Target values should be informed by accumulated evaluation data and adjusted for an agent’s risk profile, with Grafana Cloud’s Agent Observability and Grafana Assistant positioned as tools to create evaluations, recommend targets, and establish SLOs from real agent conversations.

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