Evidence-as-Code: Making AI Governance Verifiable

As autonomous AI systems move from recommendation to execution, governance can no longer remain a collection of policy documents, periodic assessments, and retrospective audit reports. An agent may select tools, access resources, delegate subtasks, modify external systems, and act across cloud and edge environments in seconds. By the time a conventional review begins, the decision context may have disappeared and the effects may already have propagated. This article introduces Evidence-as-Code as an architectural principle for making AI governance continuously verifiable. Under this principle, every consequential agent action produces structured evidence describing identity, authority, delegation, policy evaluation, execution, outcome, and subsequent control events such as revocation. Evidence is generated as part of the control path, cryptographically bound to its context, correlated across distributed components, and evaluated automatically against governance requirements. Evidence-as-Code extends Policy-as-Code and observability. Policy-as-Code expresses what should be allowed; enforcement decides what is allowed at runtime; telemetry describes what the system did; Evidence-as-Code establishes why an action was permitted, under whose authority it occurred, which controls were applied, and whether the resulting claim can be independently verified. Within SGAEIA, this creates the missing assurance layer between bounded authority, Zero Trust, authenticated delegation, and Continuous GRC. This article presents a conceptual reference model derived from architectural synthesis across AI risk management, machine-readable compliance, provenance, workload identity, observability, and software-supply-chain attestations. It does not claim empirical validation, certification, or legal conformity. This publication is Article 6 of 8 in the SGAEIA Research Series and develops the evidence and assurance layer of the Secure Governed Autonomous Edge Intelligence Architecture.