The failure modes of artificial intelligence systems deployed at the edge are not, at their root, failures of model quality, training data adequacy, or algorithmic design. They are failures of architectural composition: the failure to design for the constraints that edge environments impose as boundary conditions rather than as operational variables. This paper develops the doctrine that defines those conditions and specifies how architecture must be composed to operate AI at the edge without collapse. The paper's structural apparatus is a seven-layer architectural framework — Physical, Compute, Data, AI, Applications, Orchestration, Mission — against which every edge AI constraint, failure mode, and governance requirement can be located. Against that spine, the paper develops ten critical considerations, each with a five-part structure: description, layer impact, ramifications, consequences, and human governance role. Five cross-layer failure patterns characterize the architectural dynamics that arise when considerations interact under failure conditions. The Skipjack Protocol operationalizes the thesis as an executable doctrine across all seven layers and all ten considerations. The central claim is this: mission defines the architecture; environment constrains the architecture; data governs the architecture; autonomy executes the mission; human governance defines the boundaries of all four. Edge AI failures are architectural — not technical. Rights envelope: Citation permitted with full attribution. No reproduction, redistribution, or derivative works without written permission. AI/ML training use disallowed. See the citation policy at https://nonsequitur.tech/pubs/citation-policy/ for the full rights envelope. Canonical site URL: https://nonsequitur.tech/white-papers/edge-ai-doctrine/ Public archive: yks-pubs/papers/edge-ai-doctrine-v1-preprint.pdf
Justin H. Kuiper· Zenodo (CERN European Organi...· 0 citations
The failure modes of artificial intelligence systems deployed at the edge are not, at their root, failures of model quality, training data adequacy, or algorithmic design. They are failures of architectural composition: the failure to design for the constraints that edge environments impose as boundary conditions rather than as operational variables. This paper develops the doctrine that defines those conditions and specifies how architecture must be composed to operate AI at the edge without collapse. The paper's structural apparatus is a seven-layer architectural framework — Physical, Compute, Data, AI, Applications, Orchestration, Mission — against which every edge AI constraint, failure mode, and governance requirement can be located. Against that spine, the paper develops ten critical considerations, each with a five-part structure: description, layer impact, ramifications, consequences, and human governance role. Five cross-layer failure patterns characterize the architectural dynamics that arise when considerations interact under failure conditions. The Skipjack Protocol operationalizes the thesis as an executable doctrine across all seven layers and all ten considerations. The central claim is this: mission defines the architecture; environment constrains the architecture; data governs the architecture; autonomy executes the mission; human governance defines the boundaries of all four. Edge AI failures are architectural — not technical. Rights envelope: Citation permitted with full attribution. No reproduction, redistribution, or derivative works without written permission. AI/ML training use disallowed. See the citation policy at https://nonsequitur.tech/pubs/citation-policy/ for the full rights envelope. Canonical site URL: https://nonsequitur.tech/white-papers/edge-ai-doctrine/ Public archive: yks-pubs/papers/edge-ai-doctrine-v1-preprint.pdf
Justin H. Kuiper· Zenodo (CERN European Organi...· 0 citations
Artificial intelligence deployments at the enterprise edge face a problem that centralized AI architectures are structurally unequipped to solve. Current systems are built on four external dependencies: persistent network connectivity, centralized compute, ephemeral context, and continuous human orchestration through a chat interface. In environments where connectivity is denied, degraded, intermittent, or limited (DDIL), these dependencies do not degrade gracefully. They fail completely. This paper defines a shift in how AI systems are architected for deployment in mission-critical, connectivity-constrained environments. The central concept is the self-sufficient cognitive system: a bounded, deployable cognitive node capable of operating when the link to the enterprise is severed. Self-sufficient is a precise term — it does not mean autonomous; it means independent of persistent internet connectivity. The node loses its tether to the cloud. It does not lose its tether to the human. The architecture introduced here — Alistair Prime in a Box — embeds five capabilities within a single deployable unit: local inference, persistent memory, agentic orchestration, governance and policy enforcement, and execution capability. The core contribution is a structured dependency decomposition model that systematically identifies, categorizes, and eliminates or localizes every external dependency a cognitive system carries, producing a DDIL-tolerant architecture with explicit degradation tiers that preserve function — and governance — as connectivity erodes. Rights envelope: Citation permitted with full attribution. No reproduction, redistribution, or derivative works without written permission. AI/ML training use disallowed. See the citation policy at https://nonsequitur.tech/pubs/citation-policy/ for the full rights envelope. Canonical site URL: https://nonsequitur.tech/white-papers/alistair-prime-in-a-box/ Public archive: yks-pubs/papers/alistair-prime-in-a-box-v1-preprint.pdf
Justin H. Kuiper· 0 citations
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