The rapid adoption of Generative Artificial Intelligence (GenAI) in enterprise workflows, from customer support to document processing, procurement and knowledge retrieval, has revealed a fundamental gap in conventional quality assurance practice. GenAI components are probabilistic, context-sensitive, dependent on retrieval corpora, tool integrations and evolving model versions, unlike deterministic software. Automated testing frameworks based on the assumption of stable input-output mappings are no longer sufficient to ensure the reliability, safety and compliance of these systems. In regulated and high-stakes enterprise settings, the lack of a scalable, CI/CD-friendly testing approach creates an unacceptable risk.

In this paper, we develop an automated testing framework that combines metamorphic testing and property-based testing to validate enterprise GenAI applications at scale. Metamorphic testing identifies inconsistencies and emergent faults between related input transformations without needing to define the expected outputs a priori, directly addressing the oracle problem faced by generative systems. Property-based testing is an alternative approach that creates a range of test cases from business rules, domain invariants and constraint specifications. Simultaneously, the framework evaluates hallucination rates, accuracy of retrieval grounding, correctness of tool calls, policy compliance, and prompt regression for RAG pipelines and agentic workflows.

To demonstrate how the proposed framework's metrics would be applied and evaluated in practice, this paper additionally presents an illustrative case scenario across four representative enterprise workflows customer support triage, procurement approvals, code review assistance, and knowledge-base Q&A conceptually modeled on published, evidence-driven quality-gate approaches for LLM applications. In this constructed 24-week quasi-experimental scenario, automated testing gates are illustrated as producing a

6.7 percentage point increase in task success rate (Cohen's d = 1.52), a 27.5% decrease in escalation rate, and a 31.0% decrease in rework rate. Business error cost per 1,000 workflow instances is illustrated as decreasing by 33.4%, and incorrect tool actions by 38.5%. User satisfaction is illustrated as increasing by 0.34 points on a 5-point scale. These illustrative results are shown to hold up under difference-in-differences and segmented-regression analyses, demonstrating how a framework's effectiveness could be assessed beyond simple pre/post comparison rather than reporting outcomes of a completed deployment. Taken together, the proposed framework and illustrative demonstration position metamorphic and property-based testing as scalable, CI/CD-compatible quality assurance techniques for enterprise GenAI systems, providing a practical path to deployment readiness, regulatory traceability, and sustained operational trust.