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Decide what your AI feature must still do when its model or network is unavailable before you build the model-led path. That minimum behavior is the product’s fallback floor: a deliberate, testable guarantee, not an afterthought triggered by an outage.
Start by defining what the feature promises offline
For embedded, mobile, and intermittently connected products, inference and connectivity can be unavailable. The user-facing behavior in that state therefore sets a practical boundary on what the feature can promise. Dr. Abtin Aghagolian, co-founder and CTO of Pikd, frames the design question this way: “what does this feature do when every clever component is unavailable, and is that acceptable?”
Aghagolian’s account, published by Embedded Computing Design on August 28, 2026, describes the choices made for his team’s assistant. It is an implementation example, not a universal architecture or independent validation of the assistant’s reliability.
Begin with the supported tasks and acceptable response when the preferred inference path cannot run. Be explicit about what the feature can still do, what it must decline, and what information it can rely on. A fallback that confidently answers beyond its known limits is not a dependable floor.
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Choose fallback layers by their dependencies and limits
Aghagolian describes four rungs in his assistant, ordered from the preferred path to the most basic. The ladder illustrates one design; it is not a prescription that every product needs four layers.
| Rung | Dependencies and role | Important limit or consideration |
|---|---|---|
| On-device language model | Runs entirely on the device; presented in the account as the default and usable without a network connection. | Still depends on the model being available and usable on the device. |
| Private cloud inference | Included in the design as a cloud option. | Defaulted off in the described assistant. |
| Cloud model through a backend gateway | The company’s backend calls the cloud model rather than the device calling a vendor endpoint. | Provider credentials stay off the device; this rung still depends on network and backend availability. |
| Deterministic scripted responder | Uses no inference or network and relies only on state already held by the device. | Answers a narrow set of common requests; it cannot handle novel questions. |
The bottom rung is useful precisely because its boundaries can be defined in advance. It can answer supported common requests accurately, within bounds, and immediately, but should not pretend to understand an unfamiliar request. Decide whether to return a clear limitation, offer a supported alternative, or defer the task until a richer rung is available.
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Set one response contract for every rung
Specify the output shape before implementing model and fallback paths. If callers receive the same response structure from every rung, they do not need to know which layer produced it. The structure can carry what the application needs to act consistently, such as the answer and any status or action information your product requires.
Do not let the model’s preferred format become the system’s contract by accident. If a scripted responder must imitate a model-specific format, deterministic behavior can become unnecessarily complicated and the abstraction can leak into callers. A shared contract constrains the design above the fallback, but makes it possible to swap paths without rewriting the rest of the product around each one.
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Make degraded behavior testable without reproducing every device failure
Keep conversation flow and provider selection as pure functions where possible: given the same inputs and policy, they produce the same result without performing microphone or network I/O. Put those hardware and service interactions at the edges. This separation lets tests exercise provider choice, fallback transitions, supported and unsupported requests, and policy branches without requiring every failure to be recreated on physical hardware.
In the article, Aghagolian reports that the assistant’s engine layer had 1,053 automated tests, most covering conditions he said would be impractical to reproduce on a device. That is a first-party figure reported by the author, not an independently audited result. The useful design principle is to make failure paths deliberate and exercisable; a test count alone does not establish quality or real-world availability.
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Compare options against the product’s actual constraints
Do not declare one fallback architecture best for every feature. Compare candidate rungs using the conditions that matter to your users and implementation:
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- Dependency: Does this path require a model, network, provider, or mutable state?
- Coverage: Which common requests does it support, and how does it handle novel ones?
- Predictability: Are supported answers bounded, and does the path honor the shared response contract?
- Latency and availability: Can it respond in the degraded conditions the product must tolerate?
- Privacy and credentials: Where does inference run, and do provider credentials remain off-device?
- Verifiability: Can tests exercise its failure and policy paths without rare hardware conditions?
These are decision criteria, not benchmark results. A model path may expand coverage, while a deterministic path may offer better control over a limited set of tasks. The right balance depends on the product’s minimum promise, the consequences of a wrong answer, and which dependencies can realistically be available.
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Budget for the work of a real floor
A deterministic responder takes code and advance decisions about the minimum acceptable behavior. It may be less visually impressive than a model-led feature, but the work makes the product’s behavior during degradation explicit. Aghagolian’s standard is concise: “A floor without evidence is an assumption.” Test the supported cases, the unsupported cases, and the transitions that select each rung; then verify that every path returns the agreed response shape.
He also puts the tradeoff in user terms: “Always answers” is a substantially harder requirement than “answers well”. For a product that makes that guarantee, the fallback must be designed and verified as part of the feature, not merely described as a backup.
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