Sovereign Agentic Loops: Decoupling AI Reasoning from Execution in Real-World Systems

TL;DR

Sovereign Agentic Loops (SAL) propose a control architecture that decouples AI reasoning from execution, enhancing safety and auditability in real-world system interactions with LLM agents.

Contribution

SAL introduces a structured control-plane architecture with validation, obfuscation, and audit mechanisms to improve safety and accountability in AI system execution.

Findings

SAL blocks 93% of unsafe intents at the policy layer.

SAL prevents unsafe executions in benchmark tests.

SAL adds a median latency of 12.4 ms.

Abstract

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 Chain for auditability and replay. We formalize SAL and show that, under the stated assumptions, it provides policy-bounded execution, identity isolation, and deterministic replay. In an OpenKedge prototype for cloud infrastructure, SAL blocks 93% of unsafe intents at the policy layer, rejects the remaining 7% via consistency checks, prevents unsafe executions in our benchmark, and adds 12.4 ms median latency.