Agentic AI in Quality Engineering: Building Self-Learning Test Automation for Complex Digital Platforms

Overview

As enterprise applications become increasingly distributed and dynamic, traditional automation frameworks struggle to keep pace with continuous software changes. This white paper explains how Agentic AI has changed the game in the era of self-learning test automation, which can automatically adjust to software changes in applications without human intervention. It lays out all the problems associated with traditional automation methods, including weak scripts, maintenance costs, and the inability to scale, and presents intelligent automation approaches to testing.   
 

The white paper further explains the architecture of Agentic AI-driven testing, implementation of best practices, governance strategies, and enterprise deployment approaches. It also addresses critical considerations such as regulatory compliance, model drift, and operational challenges, helping organizations build scalable, resilient, and future-ready quality engineering ecosystems.