BEYOND THE COMPLIANCE PERIMETER: CONSTITUTIONAL RESILIENCE AND THE GOVERNANCE–INNOVATION RELATIONSHIP IN AGENTIC SOFTWARE-AS-A-SERVICE

Abstract

The integration of autonomous artificial intelligence (AI) agents into software-as-a-service (SaaS) products has moved faster than the governance methods built to manage it, and existing instruments laid useful foundations but lack the detail required for systems that act semi-independently (Adabara et al., 2025). Existing approaches to resilience in SaaS, whether expressed as regulatory compliance, infrastructural redundancy, security hardening, or third-party risk management, share a feature that has rarely been examined directly: each treats resilience as something added to the system from outside, through a ring of rules and controls that the system itself does not create or update. This article offers a conceptual contribution. To the best of the author's knowledge, it is among the first to name this shared pattern, termed here the external-perimeter paradigm, and to develop a complement to it: a framework, Constitutional Resilience, that treats resilience in agentic SaaS as a set of internal governance functions, monitoring, response, anticipation, and learning, built into the product itself. Two arguments follow. First, the external-perimeter approach, while necessary, is not sufficient on its own for systems that plan and act autonomously, because an agent tends to fail in the situations a fixed set of outside rules never anticipated. Second, and as a consequence, the common belief that governance and innovation pull against each other reflects this outside-in arrangement rather than governance itself; once governance is built into the system, it can stop slowing innovation and start supporting it. The framework is developed by careful transfer from resilience engineering, normal accident theory, high-reliability organisations, and the ecological theory of adaptive systems (Hollnagel, 2011), and is distinguished from adjacent work on self-adaptive, autonomic, and assurance-oriented governance, drawing in particular on the literatures on AI assurance and algorithmic accountability (Mökander & Floridi, 2023). The framework is then evaluated by the value it creates, for whom, and in what circumstances, with trust treated as the mechanism that turns governed autonomy into delegated action, and is accompanied by a worked illustration, a reflective evaluation, candidate metrics, and a research agenda.