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Intelligent test automation uses artificial intelligence and machine learning to improve how tests are created, executed, maintained, and optimized across the software testing process.

Unlike traditional test automation, intelligent test automation systems can adapt to application changes, reduce flaky tests, generate new tests automatically, and support continuous testing with less manual effort.

Agentic AI testing is an AI-driven approach to software testing where AI agents autonomously generate test cases, execute tests, analyze test results, and optimize the testing process using feedback loops and real-world data.

Unlike traditional test automation, agentic AI systems use multi-agent systems and autonomous decision-making to continuously adapt test scenarios, improve test coverage, and support the entire testing lifecycle with minimal manual intervention.

Autonomous software testing refers to the use of artificial intelligence and machine learning algorithms to automatically generate test cases, execute tests, maintain test suites, and analyze test results with minimal human intervention.

It extends traditional test automation by reducing manual effort in test creation, test maintenance, and regression testing, while improving test coverage and testing efficiency across the software development process.

Regression testing ensures that existing functionality continues to work after code changes, new features, or bug fixes.

A regression test is any test that is executed again after a change in the system.