Why Uptime Metrics Fall Short as Reliability Benchmarks for AI Agents

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Traditional uptime and HTTP success rates are insufficient measures of reliability for AI agent systems, as an agent can return a healthy status code while failing to complete tasks correctly or safely. Experts recommend splitting agent reliability into distinct indicator families covering task success, safety controls, latency, efficiency, and escalation behavior. Safety-related metrics, such as the rate of authorized versus unauthorized actions, must never be averaged into general availability scores, since a single policy violation can be masked by otherwise healthy aggregate numbers. Business outcomes, like whether a notification was delivered or acted upon, should be tracked separately from runtime completion using a shared decision ID to enable later reconciliation. SLO targets for agents should be grounded in product risk, user expectations, and observed baselines rather than copied from unrelated systems.

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