Sovereign Agentic Loops: Decoupling AI Reasoning from Execution in Real-World Systems
Large language model (LLM) agents increasingly issue API calls that mutate real systems, yet many current architectures pass stochastic model outputs directly to execution layers. We argue that this coupling creates a safety risk because model correctness, context awareness, and alignment cannot be assumed at execution time. We introduce Sovereign Agentic Loops (SAL), a control-plane architecture in which models emit structured intents with justifications, and the control plane validates those intents against true system state and policy before execution. SAL combines an obfuscation membrane, which limits model access to identity-sensitive state, with a cryptographically linked Evidence C...
| Authors | Jun He, Deying Yu |
|---|---|
| Primary category | cs.CR |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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