SORT-AI: Agentic System Stability in Large-Scale AI Systems Structural Causes of Cost, Instability, and Non-Determinism in Multi-Agent and Tool-Using Workflows

The deployment of agentic AI systems—multi-agent orchestrations, tool-calling pipelines, and autonomous planning architectures—introduces operational instabilities that cannot be attributed to interconnect limitations or runtime control conflicts alone. Even in systems with adequate infrastructure and coherent control planes, cost escalation, non-deterministic behavior, and soft degradation persist, pointing toward semantic coupling as a distinct failure domain. This article argues that agentic system stability—the degree to which autonomous agent decisions across planning, tool selection, execution, and verification layers remain mutually consistent with respect to shared intent—constitutes a structural property whose loss gives rise to economically significant inefficiencies. Classical metrics fail to capture stability loss because conflicts between agentic layers are semantically distributed, emergent, and do not manifest as discrete faults. The contribution of this work is a structural problem analysis that positions agentic incoherence as a first-order economic and operational variable, complementing prior analyses of interconnect-induced instability and control plane incoherence. The methodology is deliberately conceptual, avoiding implementation details, framework evaluations, or prescriptive solutions.