Maximum Viable Robustness (MVR): An Experience-Informed Framework Proposal for Governing AI-Assisted Enterprise Software Development

The Minimum Viable Product (MVP) has often been generalized beyond validated learning into a dominant delivery heuristic for software development, rational when construction effort was the binding constraint. Recent advances in AI-assisted development, under structured methodology conditions, are already substantially reducing the generation effort associated with recurring robustness patterns—multi-tenancy scaffolding, security-gated CI/CD, compliance documentation, and observability instrumentation—potentially shifting the binding constraint toward sustainability, governance, and qualified human review capacity. This paper proposes an experience-informed technical framework—Maximum Viable Robustness (MVR)—as an alternative governance approach for governed, multi-tenant enterprise platforms in contexts where this effort compression applies. MVR is defined as the highest level of capability that can be sustainably maintained under governance, without excessive dependency on waivers or recurring structural corrections. We outline a weighted governance model distinguishing design-time ceilings (MVR_nominal) from runtime operational envelopes (MVR_active), a seven-dimensional evaluation framework, a declaration protocol, and a rollback typology distinguishing strategic from emergency reduction. We formalize the Cognitive Asymmetry Ratio (CAR) and a Friction Budget model extending the SRE Error Budget to process-level structural fragility invisible to outcome-oriented SLO metrics. This paper is a conceptual and operational specification grounded in anonymized industrial practice and practitioner observation, not a controlled empirical validation. The MVR framework is intended as a governance instrument and basis for future empirical research and practitioner refinement.