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In computational materials engineering, the integration of artificial intelligence (AI) has transformed discovery pipelines from linear predictive modeling to dynamic, self-reinforcing systems capable of iterative refinement and autonomous exploration. These systems leverage active learning, reinforcement mechanisms, and closed-loop feedback to navigate vast design spaces, accelerating the identification of novel alloys, perovskites, and functional materials. However, as AI-driven workflows evolve toward greater autonomy, they introduce self-reinforcing trajectories—sequences of model updates and data acquisitions that amplify initial biases or exploratory divergences, potentially leading to inefficient resource allocation or epistemic lock-in within suboptimal subspaces. This conceptual gap lies in the absence of formalized thresholds for intervention, where human oversight or algorithmic safeguards can recalibrate discovery without disrupting momentum. Here, we introduce the Runaway Containment Dynamics (RCD) framework, a systems-level interpretive structure that maps interaction horizons between data ingestion, model inference, and discovery outputs in materials AI ecosystems. By conceptualizing thresholds as emergent properties of feedback amplification, the RCD delineates structural layers—representation stabilization, inference propagation, and trajectory modulation—alongside computational steering logics that balance exploration depth with containment precision. This framework elucidates how self-reinforcing loops, such as those in Bayesian active learning or reinforcement-driven alloy design, can be interpreted through symbolic dynamics of risk propagation, offering infrastructure-level insights for pipeline orchestration. For the field, the RCD implies a shift from reactive monitoring to proactive horizon mapping, enabling materials engineers to integrate epistemic safeguards into scalable AI infrastructures. It fosters interpretive analyses of workflow trade-offs, such as the tension between representational fidelity and inference scalability, ultimately supporting sustainable discovery logics that mitigate runaway risks while harnessing AI's generative potential. This conceptual advance positions self-reinforcing systems not as isolated optimizers but as tunable ecosystems, ripe for epistemic integration in computational materials science.