So Agentic Systems Are Messing Up Your SLO Framework

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Engineering teams running agentic systems in production tend to discover the same gap — in the same order. First they notice that the dashboards look healthy — then realize that a healthy dashboard and a correctly behaving agent are not the same thing. It is somewhere in that apparently healthy system that an agent had been quietly producing outputs that fell outside any acceptable range of correctness for hours before anyone noticed. Not because the monitoring failed to fire, but because it was watching for the wrong thing. Service level objectives (SLOs) were built on a contract that most engineering teams have never had a reason to question. Typically, a request would come in, the service would process it and reliability was measured against whether the outcome matched the expectation within the defined thresholds. That contract worked because the systems it governed were deterministic. The same input produced the same output, and deviation from that expectation was unambiguously a failure. “Agentic systems have broken that contract without replacing it with anything most platform teams are ready to operate against,” says Shahid Ali Khan of TestMu AI (formerly LambdaTest). Identical inputs can produce different, plausibly correct outputs. Quiet failures complete successfully while doing something entirely unintended — and the metrics most teams are watching — latency p99, error rate and availability — tell you whether something broke but not whether the agent deviated from the expected behavior in ways that matter to the business. Khan points out that the frameworks …

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