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Make AI optional by protecting the application’s core user outcome from model-service failures. Identify what users must still be able to do, then let AI enrich that workflow only when it responds within clear limits and its output is suitable. If the app cannot complete its central transaction without inference, AI is a dependency in the current design, whatever the code calls it.
Start by defining what must keep working
Write down the application’s core business function before choosing a fallback. A degraded mode is only useful if there is a clear success criterion: for example, a user can still submit an order, retrieve an account record, or save a draft even when an AI-generated suggestion is unavailable.
Classify each AI use by its role:
- Core: The central transaction cannot safely or meaningfully complete without inference. The feature is a hard dependency unless the product is redesigned.
- Assistive: AI improves a workflow, but users can complete it another way. Keep the underlying task available when the AI call fails and a safe alternative exists.
- Convenience: AI adds optional speed or polish. Disable or omit the feature during an outage without blocking the rest of the application.
This distinction sets the outage behavior. Do not label AI “optional” if removing it prevents the outcome users came to accomplish.
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Choose a fallback that is correct for the task
There is no universal fallback. AWS’s graceful-degradation guidance describes options such as cached or predetermined responses, while noting that degraded responses may use stale data, alternate data, or no data. The appropriate choice depends on the business impact.
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| Fallback | Use it when | Watch for |
|---|---|---|
| Serve a cached result | A previously computed answer remains valid for the task. | Staleness can make it misleading. Define acceptable age and make freshness visible where it matters. |
| Return a predetermined response | A fixed message or response can safely guide the user without pretending to be current or personalized. | Users may mistake a static answer for a live, tailored result. Label it clearly. |
| Offer a deterministic, non-AI path | Rules, ordinary search, manual entry, or another established workflow can complete the task adequately. | The alternate path may provide less convenience or value; explain the difference rather than implying equivalence. |
| Disable only the affected feature | No safe substitute exists, but the rest of the product can continue. | State what is unavailable and what users can still do. |
| Pause or route for review | A wrong or incomplete result could cause significant harm, and no safe automated fallback exists. | Define who owns the decision and how users are told what happens next. |
A responsive interface alone is not a safe outcome. In consequential or safety-sensitive workflows, disclose the limitation and stop the affected action if the application cannot produce a trustworthy result. The cited standards do not prescribe one fallback for every product; the decision depends on the task and its risk.
Bound calls so an AI outage stays contained
Treat model providers like any other external dependency: they can be slow, unavailable, throttled, or return unusable output. Keep failures from stalling unrelated workflows or consuming capacity needed by the core application.
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- Set a bounded timeout for synchronous inference calls. Do not let a user-facing request wait indefinitely.
- Avoid unbounded retries. Repeated calls during an outage can add latency and load instead of restoring service.
- Use circuit breaking or throttling where appropriate to limit repeated failing calls and protect other components.
- Make cache rules explicit, including how old a result may be and what happens when it exceeds that age.
- Decide whether work should be completed partially, queued, disabled, or sent for human review; assign an owner for that policy.
NIST SP 800-204A identifies load balancing, circuit breaking, throttling, and continuous service-health monitoring as resilience mechanisms for microservices. These mechanisms help contain dependency failures; they do not determine whether a particular fallback is correct for your users.
Make the failure path simpler—and test it
A fallback adds little resilience if it depends on the same failing service or a complicated chain of additional components. AWS advises that failure pathways be tested and “should be significantly simpler than the primary pathway.”
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- Simulate provider unavailability. Confirm that the core workflow still reaches its defined success state and that the affected AI feature takes its intended fallback.
- Exercise other failure modes. Test slow responses, provider throttling, malformed output, and failures in any cache or alternate path the fallback relies on.
- Check the user experience. Verify that the application explains what is unavailable, avoids presenting stale or static content as fresh, and does not claim a task succeeded when it did not.
- Test recovery. Confirm the application resumes normal behavior without a surge of retries when the provider becomes healthy again.
Instrument the AI boundary so operators can distinguish a model-service problem from a product-wide outage. Track request latency, timeouts and errors, circuit-breaker state, fallback activation, cache age when relevant, and user-visible completion. AWS identifies failure to define core functionality and failure to test during dependency outages as anti-patterns in its reliability guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reliability is only one part of AI risk
Availability controls do not address every risk introduced by an AI feature. Sending data to an external model service also raises privacy and security questions; output may be malformed or unsuitable even when the provider is operating normally. Assess those issues alongside continuity, correctness, latency, freshness, recovery complexity, and user expectations.
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- NIST’s AI Risk Management Framework is a voluntary framework for managing risks in the design, development, use, and evaluation of AI systems. NIST says version 1.0 is under revision; check the current status when applying it.
- OWASP AISVS provides testable security requirements for AI-enabled systems. Its project page describes version 1.0, released in June 2026, as containing 191 requirements across 12 chapters and three appendices.
- NIST SP 800-218A, published in July 2024, extends secure software development practices with AI-specific considerations for generative AI and dual-use foundation models.
NIST’s AI RMF resources describe profiles as a way to tailor framework functions and categories to a setting’s requirements, risk tolerance, and resources. The page also reports participation by more than 240 organizations in framework development; that figure describes the development process, not measured reliability outcomes.
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