Abstract : Production deployments of multi-agent AI systems have exposed a failure class that modular design alone cannot prevent: cascading errors, where a degraded output from one agent propagates through downstream components and amplifies into system-wide instability. Existing orchestration frameworks resolve execution ordering reliably but offer no mechanism to govern the reliability of intermediate outputs, leaving a structural gap in failure governance that grows more consequential as pipeline complexity scales. This paper introduces the Causal Intervention Control Plane (CICP), a conceptual framework addressing cascading failures through three coordinated capabilities: a graph-based model of failure propagation, a causal attribution mechanism that assigns quantified Causal Responsibility Scores to agents, and a set of cost-aware intervention primitives that contain and correct failures without full pipeline disruption. An Intervention Cost Model and Failure Severity Score govern when interventions are applied, ensuring corrective actions remain within the latency budgets of real-time operating environments. The CICP establishes a theoretical foundation for control-oriented multi-agent AI architectures — systems that govern their own reliability rather than deferring failure management to external monitoring.