Agentic AI testing utilizes autonomous artificial intelligence systems that can independently manage the software quality assurance process, from test creation to execution and adaptation, without the need for manual scripting. These AI agents employ large language models and machine learning to understand application contexts and adapt to changes automatically, which helps reduce test maintenance and accelerates release cycles by automating repetitive tasks. The technology offers several benefits, including enhanced testing efficiency, reliability, and scalability, and it integrates seamlessly with existing DevOps workflows. However, it also presents challenges such as inconsistent results, the need for regular retraining, and high infrastructure costs. Agentic AI testing is particularly beneficial for large, dynamic applications that undergo frequent updates, although it demands careful implementation and monitoring to ensure security and effective integration with legacy systems.