AI Software Production Systems (ASPS) — Research V4

AI Software Production Systems (ASPS) proposes that the future of enterprise software will not be defined by writing more code faster, but by the governed transformation of human intent into production-proven, legally accountable, cyber-resilient, compliant, and reversible system behavior. As autonomous AI agents move from code assistants to production actors, traditional paradigms — SDLC, Agile, DevOps, GitOps, MLOps, and Platform Engineering — leave critical gaps in trust, reversibility, and accountability that this work addresses directly.

This thesis introduces two core contributions. First, the CTQOST-R optimization model, which reframes production success as correctness per unit cost and time under explicit constraints of Quality, Operability, Security, Trust, and Reversibility — elevating reversibility to a first-class engineering invariant rather than an afterthought. Second, Reversibility-by-Design, the principle that no autonomous action is promotable to production unless its reversal, compensation, containment, or safe-degradation path is defined in advance.

ASPS organizes autonomous production into eleven interdependent governance domains — including Intent Governance, Context Integrity, Agent Engineering, Verification Factory, Evidence Ledger, Runtime Truth, Cybersecurity, Compliance, Legal Governance, and Economics — converging on a single objective: production trust. The framework is presented as a board-grade reference architecture for organizations adopting autonomous software production at enterprise scale, where a single weak domain lowers the trustworthiness of the entire system.