Originally published on AI Tech Connect.
What you need to know AI incidents are their own category. A hallucination spike, a jailbreak or a regression after a model upgrade do not look like a classic 500-error page. Your existing on-call playbook will not catch them. Detection is a sampled scorer plus your SLOs. Grade a small slice of live traffic for quality, watch latency, error and cost against thresholds, and fold in user-report signals — tuned so the pager fires on real regressions, not noise. Severity drives everything. SEV1 to SEV3 decides who is paged, how fast, and how loudly you communicate. Write worked examples for each level so nobody improvises at 2am. The levers are failover, kill-switches, rollback, rate-limits, safe mode and cached fallbacks. Each incident type has a first mitigation and a rollback path —…
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