Purpose
Design graceful degradation for short failures and documented continuity options for sustained disruption—so AI-dependent journeys fail safely and recover within defined RTO/RPO.
Engineering philosophy
Assume providers fail. Timeouts, circuit breakers, and degraded modes handle minutes; backups, failover, and human procedures handle hours and days. Safety properties must hold in every degraded mode.
Why it matters
Naive retries and single-provider dependency turn rate limits into outages; automation without continuity plans leaves the business stranded when AI is unavailable.
Common failures
- No timeout on model calls
- Infinite retries that worsen rate limits
- No fallback when the primary model is unavailable
- Agent continues after a failed critical tool as if it succeeded
- No manual fallback for AI-automated workflows
- Single provider with no contractual or technical alternative
- Backups that exclude vector indexes and prompt registries
- Untested continuity plans
Mandatory checks
Gate controls. Each check is pass/fail via artifact + pass condition. Expected from the annotated capability level when the system meets the minimum criticality tier.
All model and tool calls shall have timeouts and bounded retries
- Artifact
- Client config / static analysis report for timeouts and max retries
- Pass condition
- 100% of model/tool client call sites have finite timeout and max-retry ≤ bound; verified by static check or integration test
Critical user journeys shall define a degraded mode when AI is unavailable
- Artifact
- Degraded-mode spec per critical journey + feature-flag or fallback test
- Pass condition
- 100% of critical journeys document degraded behavior; failover test shows non-AI or safe fallback activates when AI dependency fails
Partial tool failures shall be detected and shall prevent false-success continuation
- Artifact
- Agent/tool error-handling tests for partial failure
- Pass condition
- Tests inject tool failure after partial success; agent does not report overall success in 100% of cases without explicit compensation/approval
Critical AI-dependent processes shall have documented continuity options
- Artifact
- Continuity options doc per critical process (failover, manual, alternate provider)
- Pass condition
- 100% of processes marked critical-AI-dependent have ≥1 documented continuity option with owner
Backups shall include AI control-plane artifacts needed to restore service
- Artifact
- Backup job config covering prompt registry, policies, indexes as required + restore test
- Pass condition
- Backup inventory includes required AI control-plane artifacts; restore test in last 90 days succeeds for a sample artifact set
RTO/RPO objectives shall exist for critical AI features
- Artifact
- RTO/RPO catalog for critical AI features
- Pass condition
- 100% of critical AI features have numeric RTO and RPO documented and linked to a tested restore/failover procedure
Chaos experiments shall cover AI dependency failure modes
- Artifact
- Chaos experiment plan + dated after-action report including AI provider/tool failures
- Pass condition
- PASS if at least one AI-dependency chaos exercise completed in the last 180 days with retained actions
Contractual and technical multi-provider options shall exist for Level 5 continuity
- Artifact
- Provider contract summary + technical failover design and test evidence
- Pass condition
- PASS if Level 5 workloads have a documented alternate provider/path and a successful failover test ≤180 days
Recommended checks
Strengthen posture beyond the gate. Same measurable structure; non-blocking unless elevated by organizational policy.
Circuit breakers and bulkheads around provider clients
- Artifact
- Client library/config showing circuit breakers and bulkheads around model/provider calls + last trip log
- Pass condition
- Breaker opens under induced failure in test or observed prod trip within 90 days; bulkhead limits concurrent calls per provider dependency
Multi-provider or multi-region fallback with eval coverage
- Artifact
- Multi-provider or multi-region fallback config + eval report comparing primary vs fallback quality/safety
- Pass condition
- Fallback path is configured and was exercised ≤90 days; fallback eval meets minimum quality/safety bars (not only connectivity)
Periodic continuity drills including provider loss
- Artifact
- Continuity drill calendar + last provider-loss drill report with RTO/RPO results
- Pass condition
- Provider-loss (or equivalent) continuity drill completed ≤90 days; RTO/RPO met or exceptions have named owners and expiry
Warm standby for self-hosted inference where required
- Artifact
- Warm-standby inference architecture + last failover test report with RTO result
- Pass condition
- Failover to warm standby completes within documented RTO in the last test (≤90 days); standby capacity covers declared peak for critical workloads
Evidence required
- Timeout/retry configuration
- Degraded-mode UX and behavior documentation
- Postmortems referencing resilience controls where applicable
- Continuity plans for critical journeys
- Backup/restore test records
- RTO/RPO definitions
Severity & risk
- Severity
- high
- Impact if violated
- Risk level
- high
- Typical residual risk (impact × likelihood)
Engineering best practices
- Idempotent tools where retries are possible
- Queue and backpressure for bursty AI workloads
- Fail safe: prefer refusal over incorrect action under uncertainty
- Separate availability SLOs for AI features vs core non-AI paths
- Identify which AI features are critical vs optional
- Design human-operated fallback procedures before full automation
- Replicate prompt registries and policies, not only databases
- Align continuity with resilience and rollback pillars
Automatic validations
- Synthetic canaries against providers
- Alerts on elevated timeout and 429 rates
- Integration tests for fallback paths
- Scheduled backup success checks
- Restore tests in isolated environments
- Health checks on standby providers
Manual validations
- Game days simulating provider outage
- Review of agent compensation logic after tool failure
- Business impact analysis for AI features
- Executive review of continuity acceptance risk
Examples
- If the LLM provider is down, search falls back to keyword results with a clear banner
- An agent aborts a payment flow when the ledger tool times out rather than guessing success
- If the LLM provider fails, claims adjusters switch to a documented manual checklist
- Prompt registry backups restore a prior known-good configuration in a DR region
References
Crosswalks
MANAGE Manage
NIST AI Risk Management Framework · supports
Secure & Resilient Secure and Resilient
NIST AI Risk Management Framework · aligns-with
LLM10 Unbounded Consumption
OWASP Top 10 for Large Language Model Applications · supports
CC7 System Operations
SOC 2 Trust Services Criteria · evidence-for
CC9 Risk Mitigation
SOC 2 Trust Services Criteria · evidence-for
A1 Availability
SOC 2 Trust Services Criteria · evidence-for
Reliability Reliability
AWS Well-Architected Framework · aligns-with
Future evolution
Portable health/degradation protocols and multi-provider continuity playbooks for AI workloads.