Agentic AI–Driven Quality Engineering: A Global Innovation Framework for Autonomous Enterprise Decision Validation

Abstract

The rapid evolution of enterprise software systems characterized by continuous delivery pipelines, large-scale data integration, and AI-enabled decision processes has exposed fundamental limitations in traditional quality assurance (QA) and validation practices. Manual and rule-based automation approaches struggle to scale across complex, distributed environments where system behavior, data patterns, and operational risks evolve continuously. This paper presents an Agentic AI--Driven Quality Engineering framework that redefines quality engineering as an autonomous, intelligent capability for enterprise decision validation.

The proposed framework leverages agentic artificial intelligence, in which multiple autonomous agents collaboratively execute and govern core quality engineering functions, including test orchestration, enterprise data integrity validation, anomaly detection, defect triage, and release-readiness assessment. Unlike conventional QA frameworks that rely on static test assets and predefined logic, the agentic approach incorporates adaptive reasoning and learning mechanisms that evolve based on historical defects, system telemetry, and operational feedback.

Designed for seamless integration with CI/CD pipelines and enterprise data platforms, the framework enables continuous quality monitoring and near real-time decision support. A decision confidence scoring mechanism is introduced to quantify release readiness and operational risk, providing transparent and governance-aligned insights for engineering and leadership stakeholders. The framework is evaluated through enterprise-scale validation scenarios involving complex system integrations and high-volume datasets, demonstrating measurable improvements in defect detection efficiency, reduction in manual validation effort, and accelerated decision cycles compared to traditional QA models.

By positioning quality engineering as an AI-driven, autonomous function, this research contributes a scalable global innovation framework for building trustworthy, resilient, and decision-ready enterprise systems. The proposed approach is broadly applicable across domains such as healthcare, telecommunications, finance, and large-scale digital platforms