Decide what your AI feature must still do when inference or connectivity is unavailable before you build its model-led path. That minimum behavior is the product’s fallback floor: a deliberate, testable guarantee rather than an afterthought. Dr. Abtin Aghagolian, Pikd co-founder and CTO, frames the key question this way: “what does this feature do when every clever component is unavailable, and is that acceptable?”
Start with the behavior users can count on
For embedded and intermittently connected products, loss of a model or network is a condition the design must account for, not merely an exceptional error. The fallback determines what the product can still promise in that condition. Aghagolian writes that “Always answers” is a substantially harder requirement than “answers well”; it changes the system from a best-effort model interface into a product with a defined minimum behavior.
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That does not mean every feature needs to answer every question offline. It means deciding in advance which useful actions remain available, which requests the feature cannot support, and how it communicates that boundary. A narrow, accurate response—or an explicit refusal to handle a request—can be more dependable than an unconstrained answer that silently depends on unavailable services.
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Aghagolian describes a four-rung ladder for his team’s assistant, ordered from preferred to most basic. It is an example, not a universal architecture: another product may need fewer layers or a different ordering.
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| Rung | What it depends on | What it offers and where it stops |
|---|---|---|
| On-device language model | Local model execution; the author presents this as usable without a network connection. | The assistant’s default model-led path. Its availability still depends on the device being able to run the model. |
| Private cloud inference | A cloud inference service and connectivity. | Wired into the design but, according to Aghagolian, currently defaulted off. |
| Cloud model through the company’s backend gateway | Connectivity, the company backend, and a cloud model. | Routes requests through the company’s gateway rather than directly to a vendor endpoint, keeping provider credentials off the device. |
| Deterministic scripted responder | No model or network; only state already held by the device. | Handles a narrow set of common requests within defined bounds. It cannot answer novel questions. |
The bottom rung is the dependable floor in this example because it does not rely on inference or connectivity. Its value is not broad conversational ability; it is immediate, bounded behavior for requests the team has chosen to support. The trade-off is deliberate work: engineers must decide what the minimum behavior should be and implement those responses even though the result may be less visually impressive than a model feature.
Define one response contract for every rung
Specify the shape of a response before implementing the layers. If every rung returns the same contract, callers can consume the result without knowing whether it came from a local model, cloud service, or scripted responder. That makes it possible to change providers or degrade gracefully without spreading provider-specific assumptions through the rest of the application.
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The contract also sets a constraint: model-generated output must fit the format the fallback can produce. If the model path invents a complex response shape that the deterministic path must imitate, the fallback becomes harder to build and the abstraction can leak. Define what a caller needs—such as a response, a supported action, or a clear indication that a request is outside the fallback’s scope—and keep that meaning consistent across rungs.
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Aghagolian says his team separated the conversation loop and provider-selection logic into pure functions, keeping microphone and network interaction at the edges. With policy and selection decisions isolated from I/O, engine logic can be exercised across provider choices and degradation branches without physically reproducing every device condition.
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
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Tests should cover the transitions and boundaries that define the product’s guarantee, not just the successful model response. Useful cases include:
- Inference is unavailable and the request matches a scripted response.
- Inference is unavailable and the request is outside the supported set.
- A preferred provider cannot be used and the selection policy chooses another available rung.
- Each rung returns output in the same contract expected by callers.
- Connectivity-dependent paths are not treated as available when the network is absent.
Aghagolian reports 1,053 automated tests in the assistant’s engine layer, with most covering conditions impractical to reproduce on a device. That count is his first-party report in the article, not an independently audited test result; it is evidence of the team’s reported testing practice, not proof of uptime or general performance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate the ladder against the actual product need
There is no single best fallback architecture for every AI feature. Compare candidate rungs against the product’s requirements rather than assuming that more layers automatically mean greater reliability.
- Dependency: Does the rung require a model, network, provider, or mutable state?
- Coverage: Which recurring requests can it handle, and which novel requests must it decline or send elsewhere?
- Predictability: Are supported responses bounded and correct, and do they follow the shared contract?
- Availability and latency: Can the rung respond offline or during degraded service, and what must be functioning first?
- Privacy and credentials: Where does inference run, and do provider credentials remain off the device?
- Verifiability: Can policy choices and failure branches be exercised deliberately in automated tests?
These are design questions, not benchmark results. The right floor is the smallest behavior that remains useful and acceptable for the specific feature, with limitations users and callers can understand.
Turn the floor into a product guarantee
Before expanding model capability, write down the supported fallback requests, the cases that must be declined, the response contract, and the conditions under which each rung is available. Implement the deterministic path, then test that provider selection and failure handling preserve the contract as dependencies disappear. Aghagolian’s concise warning is: “A floor without evidence is an assumption.”
The account is Aghagolian’s description of his team’s assistant, published by Embedded Computing Design on August 28, 2026. Its reported architecture and test count should be understood as that team’s account, not independent validation or a guarantee that the same ladder suits every embedded AI system.
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