AI Failover
Governed transition of decision responsibility between AI systems when integrity, reliability, or operating conditions degrade—designed for regulated, safety-critical environments.
What AI failover means
AI failover is the managed transition of decision responsibility between AI systems when performance, reliability, or operating conditions change. Unlike infrastructure failover, the objective is not just continuity, but maintaining trustworthy operation where outputs can affect real-world systems.
In practice, validation methods (shadow execution, replay, canary, and hybrid approaches) generate evidence—then governance uses that evidence to decide whether authority may change.
Promotions can require both a pre-promotion validation window and a promotion alignment window immediately prior to activation to prevent last-moment divergence.
Authority governance
Separate validation from operational control—authority transitions are explicit, governed, and observable (no implicit handoffs).
Continuous oversight
Monitor behavior under real operating conditions using multiple signals to support informed governance decisions.
Degradation-triggered governance
When degradation is detected, constrain authority and govern transitions using validation thresholds and auditable controls—before outcomes are affected.
Audit readiness
Maintain reviewable records of triggers, authority changes, and operating context with integrity protections.
Why traditional failover is insufficient
In safety-critical environments, an AI system can remain “online” while quality degrades gradually. Incorrect outputs can be worse than an outage—so transitions must be governed and audit-ready.
Silent degradation
Systems may appear healthy while output quality drifts over time.
Operational risk
Incorrect decisions may create physical, financial, or regulatory exposure.
No restart dependency
Recovery cannot rely solely on restarts or downtime-based restoration.