Lifecycle Governance as a Robustness Mechanism: Bridging Agentic AI Failure Modes and Institutional Control Architectures

Agentic AI systems introduce failure modes distributed across multi-step workflows—perceive, plan, act, reflect, and learn—such that no single-point technical defense is sufficient for robustness. Separately, institutional AI governance literature argues that static compliance frameworks are insufficient for operational stability, requiring continuous, lifecycle-spanning oversight mechanisms. This paper offers a heuristic reading of structural parallels between these two bodies of preprint work; it does not derive equivalences from a shared formal structure. Drawing on a preprint survey of agentic AI trustworthiness, a preprint governance control stack framework, and a preprint deployment assurance framework, we identify four candidate convergences: (1) the per-stage risk distribution in agentic systems and the lifecycle-phase structure of institutional governance share a partial structural alignment that may facilitate targeted transfer of mitigations, though comprehensive isomorphism is not established; (2) capability-aware oversight—scaling governance intensity with system capability—is a principle present in both domains but underspecified in each; (3) continuous auditing mechanisms in institutional governance share functional overlap with iterative post-action reassessment in agentic safety, yet differ in operational scope; and (4) safety and robustness share defense mechanisms in agentic systems and should be co-designed rather than treated as orthogonal. These convergences are offered as a research agenda rather than established findings, and each is accompanied by a specific falsification path. Authorship: Saluca Agentic AI Research Team (Saluca LLC). AI-drafted from arXiv preprint corpus on the date in the filename. Cited arXiv preprints: 2503.12687v1, 2508.02866v3, 2512.11931v1, 2604.03262v1, 2605.27827v1