The Responsibility Cascade: Moral Attribution and Governance Challenges in Agentic AI Systems

The emergence of agentic AI systems that can pursue complex goals through multi-step planning and adaptive execution presents new challenges for attributing moral responsibility for the products developed by human-agentic AI teams. While the “responsibility gap” literature examines blame attribution for AI outcomes and the “moral crumple zone” concept identifies how humans absorb misattributed responsibility, neither framework adequately addresses the temporal distribution of moral responsibility across sequential agentic workflows in human-AI teams. This article introduces the responsibility cascade, a phenomenon whereby moral responsibility diffuses, dilutes, and redistributes across the sequential steps of an agentic AI workflow, contrary to traditional moral attribution frameworks. Drawing on philosophical accounts of guidance control, collective agency, and organizational responsibility, we argue that agentic AI creates chains of delegated agencies that fundamentally transform how responsibility propagates through human-AI collaborations. We propose a Sequential Responsibility Attribution Framework (SRAF) that distinguishes four modes of responsibility distribution in agentic workflows: initiation responsibility, checkpoint responsibility, delegation responsibility, and outcome responsibility. This framework provides diagnostic value by identifying responsibility gaps and normative guidance for designing agentic systems that preserve meaningful human control and avoid moral crumple zones. Beyond its philosophical contribution, the framework has direct implications for AI governance, informing how regulators, organizations, and system designers can preserve accountability as agentic AI becomes prevalent in consequential domains including healthcare, finance, software development, and critical infrastructure.