# A Control-Theoretic Foundation for Agentic Systems

arXiv: https://arxiv.org/abs/2603.10779 · HTML: https://mlsanity.com/paper/2603.10779/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2026.

## Built on by (most cited first, 4 of 4)

- [QUIVER: A Formal Framework for Quantifying Perturbation Propagation and Bifurcation in Compound AI Systems](https://mlsanity.com/paper/2605.23956/) (2026)
- [Tangent: An Empirical Study of Testing Practices for LLM-Based Agent Applications](https://mlsanity.com/paper/2608.08413/) (2026)
- [When Agents Meet Electric Bus Fleet Operations: Pricing Behavior, Trade-offs, and Policy Implications in an Aggregator Framework](https://mlsanity.com/paper/2606.26400/) (2026)
- [Epi-Logic: A Conceptual Framework for Epistemic Runtime Control, Schema Validity Checking, and Controlled Accommodation in Autonomous AI Agents](https://mlsanity.com/paper/2609.24755/) (2026)

## Abstract (arXiv)

This paper develops a control-theoretic framework for analyzing agentic systems embedded within feedback control loops, where an AI agent may adapt controller parameters, select among control strategies, invoke external tools, reconfigure decision architectures, and modify control objectives during operation. These capabilities are formalized by interpreting agency as hierarchical runtime decision authority over elements of the control architecture, leading to an augmented closed-loop representation in which physical states, internal memory, tool outputs, interaction signals, and design variables evolve as a coupled dynamical system. A five-level hierarchy of agency is defined, ranging from fixed control laws to runtime synthesis of control architectures and objectives. The analysis shows that increasing agency introduces interacting dynamical mechanisms such as time-varying adaptation, endogenous switching, decision-induced delays, and structural reconfiguration. The framework is developed in both nonlinear and linear settings, providing explicit design constraints for AI-enabled control systems in safety-critical applications.