AGENTIC COLLAPSE: A Time-Delayed Cybernetic Framework for Epistemic Stability in Autonomous AI Systems

The transition from static Large Language Models to dynamic Autonomous Agents has introduced a new class of epistemic failure modes driven by the “Orchestration Penalty”—the factorial increase in verification complexity inherent to multi-step reasoning loops. This research identifies a structural “Latency Killer” in System 2 architectures, where the velocity of agentic action ($\phi$) inherently outpaces the latency of deliberate verification ($\tau_{sys2}$). We introduce a time-delayed stochastic dynamical system ($d\phi, d\psi, dU$) that models the accumulation of “Agentic Drift.” Simulation results reveal a catastrophic phase transition termed “Agentic Collapse,” where a stochastic complexity spike ($t \approx 60$) drives the system into an irretrievable high-entropy state before the lagged verifier can compensate. We demonstrate that perfect stability is mathematically impossible in open-ended agentic loops; instead, safety requires a regime of “Metastability” enforced by a “Popperian Guillotine”—a discrete safety interlock that resets the agent when epistemic potential exceeds a critical threshold ($U_{crit}=0.88$). Finally, we derive an “Epistemic Speed Limit,” arguing that robust autonomy requires dynamically throttling execution speed to match the “Cost of Thought.”