Edge AI Doctrine: Ten Critical Considerations for Edge AI, With Architectural Ramifications, Consequences, and Governance

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