Cybernetic Agents: Semantic Control Theory for Robust and Safe Large Language Model Agents

Abstract

Large Language Model (LLM) agents are increasingly deployed in dynamic, partially observable environments, where they must reason, plan and act over long horizons. However, current LLM-based agents are typically designed as open-loop systems: they rely on prompt engineering and heuristic tool-calling pipelines, lack explicit state estimation, and provide no formal guarantees on stability, robustness or safety. This proposal introduces Cybernetic Agents, a unified framework that applies semantic control theory to LLM agents. We propose five tightly coupled components: (A) a formal Semantic Control Theory that defines semantic state spaces, Lyapunov-style stability, robustness and safety on embedding manifolds; (B) a closed-loop Cybernetic-Agent Architecture that organizes observer, planner, regulator, safety filter and executor around the LLM core; (C) an LLM-based Model Predictive Control (LLM-MPC) module for receding-horizon planning in semantic space; (D) a Semantic Kalman Filter for belief-state estimation and hallucination drift correction; (E) a Control Barrier Function (CBF)-based semantic safety filter that enforces forward invariance of safe sets at the token and action level. The proposal develops a detailed mathematical formulation of LLM agents as stochastic semantic dynamical systems, specifies the Cybernetic-Agent framework with explicit equations and control-theoretic structure, outlines a Lyapunov-based analysis of semantic stability and CBF-based safety, and designs an experimental program on OS-level, web-based, and safety-focused benchmarks. The long-term goal is to transform LLM agent design from empirical prompt-tuning into a principled control-engineering discipline.

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