Assembled builds automated LLM fallback system achieving 99.97% effective AI uptime
LLM provider outages caused multiple customer-impacting incidents at Assembled, and manual model switchovers were slow, stressful, and unable to handle partial degradations — leaving the team reactive rather than resilient.
A first attempt using on-call engineers with an accessible configuration switch failed because blanket switches broke nuanced per-model routing, took too long, and could not reliably classify partial versus full outages.
Automated fallbacks achieved 99.97% effective uptime on AI model responses, reduced average failover time from 5+ minutes to hundreds of milliseconds, and eliminated manual interventions during provider outages — with request failure rates below 0.001% during a recent multi-hour outage.
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Frequently asked questions
What did this team achieve with this AI workflow?
Automated fallbacks achieved 99.97% effective uptime on AI model responses, reduced average failover time from 5+ minutes to hundreds of milliseconds, and eliminated manual interventions during provider outages — with…
What tools did this team use?
OpenAI, Anthropic, GPT-4.1-Mini, Claude 3.5 Haiku, Gemini 2.5 Flash.
What results were reported?
effective AI uptime: 99.97%; Average failover time after automation: hundreds of milliseconds; Average failover time before automation: 5+ minutes; Manual interventions during provider outages: zero (source-reported, not independently verified).
What failed first in this deployment?
A first attempt using on-call engineers with an accessible configuration switch failed because blanket switches broke nuanced per-model routing, took too long, and could not reliably classify partial versus full outages.
How is this back office ops AI workflow structured?
Provider failure detected → Streaming state check → Category-consistent fallback routing → Fallback model processes request → Response delivered to user.