Cyclic Distinction–Integration as a Governing Framework for Intelligent AI Delegation: A Control-Theoretic Extension to Multi-Agent Authority Transfer

Multi-agent AI systems require delegation protocols that govern not only task allocation but also the transfer of authority, accountability, and trust across agent networks. The recently published DeepMind framework for Intelligent AI Delegation (Tomasev et al., 2026) identifies dynamic trust calibration, accountability chain integrity, and adaptive failure handling as essential requirements, yet provides no formal temporal mechanism specifying when delegation cycles should reinitiate.This paper proposes that Cyclic Distinction–Integration (CDIL), a control-theoretic framework previously applied to cancer therapy optimization, provides precisely this missing temporal architecture. Agent trust capacity is formalized as a buffering parameter analogous to cellular buffering capacity, and delegation trust dynamics under phase separation are shown to reduce to an affine contraction mapping with tunable load–recovery asymmetry.The paper establishes: (1) Lemma 3.4 — existence and uniqueness of a stable periodic governance regime under CDIL cycling; (2) Theorem 3.6 — parameter-bounded delegation differential accumulation, proving that high-trust agents converge to governance homeostasis while low-trust agents accumulate revocation-triggering debt with provable lower-bounded linear growth; and (3) a formal comparison showing that continuous reassessment regimes converge to undifferentiated equilibria that lack the differential amplification mechanism of cyclic governance.The framework is mapped to the five core requirements of the DeepMind delegation architecture, four testable predictions are derived, and a minimal simulation framework is specified. Limitations including endogenous demand dynamics and strategic agent behavior are identified as directions for future work.