AI Failover
Controlled transfer of operational authority between AI systems when safety, integrity, or conditions degrade—built for regulated, safety-critical environments.
What AI failover means
AI failover is the deliberate transfer of decision authority between AI models or AI subsystems when performance, integrity, or operating conditions degrade. Unlike infrastructure failover, the goal is not just uptime—it's maintaining validated authority where outputs can affect real-world operations.
Authority awareness
Separate parallel evaluation from operational authority—transfers are explicit, controlled, and observable.
Continuous validation
Evaluate candidate behavior under real operating conditions before granting control.
Degraded-state handling
Detect partial degradation early and respond before drift becomes operational risk.
Auditability
Maintain reviewable records of authority changes, triggers, and decision context.
Why traditional failover is insufficient
In safety-critical environments, a model can remain “online” while silently degrading. Bad outputs can be worse than an outage—so authority transfer must be explicit and auditable.
Silent degradation
Systems can appear healthy while output quality drifts over time.
Output risk
Incorrect decisions may cause physical impact or compliance exposure.
No restart dependency
Recovery cannot rely on reboots or downtime-based restoration.