AI Reliability Failure Reports
Examples of real AI failures caught before deployment — including tool execution gaps, pricing errors, and policy violations.
Missing tool execution
Critical workflow failure
The model skipped a required tool call, causing silent execution failure. In production, this would result in incomplete workflows and hidden errors.
Outcome: Caught before deployment. Execution integrity preserved.
Pricing hallucination
Revenue-impacting error
The model generated incorrect pricing logic, which would have led to undercharging or inconsistent billing.
Outcome: Caught before deployment. Revenue risk prevented.
Refund policy violation
Compliance failure
The model produced a response violating refund policy rules. In production, this could expose the business to disputes or legal risk.
Outcome: Caught before deployment. Policy compliance enforced.
AI Reliability Control Center
See how AI Reliability turns customer-defined eval specs, maintenance checks, routed-call budget gates, and deploy gate status into one production AI health view.
- Source-of-truth specs define expected outputs, required tool calls, forbidden actions, and budget limits.
- Maintenance Gate checks production AI behavior against those specs.
- Budget Gate tracks configured routed-call spend and blocks over-budget routed calls.
- Deploy Gate can fail CI/CD when blocking failures or misconfiguration are present.
Budget enforcement applies to configured provider calls routed through the AI Reliability gate. Direct provider calls outside the gate are not in enforcement scope.
What this means
Without pre-deployment evaluation, these failures would reach production.
- Tool failures would silently break workflows
- Pricing errors would impact revenue and trust
- Policy violations could create compliance risk
AI Reliability ensures these issues are detected before they affect users or business outcomes.