AUTONOMOUS AGENTIC MARKETING NETWORKS AND THE RISK BOUNDARIES OF REAL-TIME RESOURCE REALLOCATION

Abstract

The paradigm of digital market intermediation is shifting from deterministic automation to autonomous agentic networks. These systems, driven by large action models (LAMs) and multi-agent reinforcement learning (MARL), execute real-time resource reallocation across fragmented media ecosystems without continuous human oversight. While this shift significantly enhances allocation efficiency, it introduces profound systemic vulnerabilities. This paper establishes a comprehensive conceptual framework to examine the risk boundaries of autonomous marketing networks. By synthesising systems theory, algorithmic game theory, and data governance frameworks, this study categorises the primary risk vectors: propagation, autonomy, persistence, and emergence. The analysis reveals how closed-loop algorithmic feedback structures can trigger catastrophic capital misallocation and brand degradation before human intervention can occur. Finally, this research proposes an architectural governance model based on real-time cybernetic circuit breakers, dynamic policy constraints, and cryptographic provenance trails to mitigate systemic instability in autonomous enterprise marketing.