The coming decade of computing will be defined less by ever larger modelsand more by whether agentic LLM systems can be deployed with sovereignty,resilience, predictable cost, and auditable accountability. Cloud-centric LLMstacks concentrate control, couple critical decisions to connectivity and pro-vider updates, and obscure multi-step tool use, exactly where governance andtrust are hardest. In safety-critical and regulated settings, this shift is no long-er optional, it is inevitable. Meanwhile, edge devices with NPUs and heteroge-neous accelerators are making local, quantized inference and lightweight or-chestration practical, opening a path toward sovereign assistants that remaininside the organizational boundary.This paper introduces Sovereign Edge Agentic LLM systems (SEALs),node-bound, role-constrained agents that run primarily on local hardware andlocal data, continue to operate under intermittent connectivity, and produceverifiable evidence for each decision. SEALs integrate (I) local retrieval forgrounding in curated knowledge, (II) a controlled tool layer that restricts ac-tions to approved capabilities, and (III) a decision-assurance layer that emitsa verifiable “decision book” linking outputs, evidence, tool calls, and calibrat-ed confidence signals. We argue that SEALs are a critical building block forfuture human-centric computing, where AI assistants must be accountable bydesign, not by after-the-fact policy.
Jovan Ivković (Thu,) studied this question.