What are the most valuable AI-powered tools available today for assisted automation?
How does Agentic Testing differ from traditional automation testing approaches?
How effective are self-healing tests in reducing maintenance overhead?
How important is domain knowledge for agentic testing?
How should a tester prioritize which tests to execute manually versus automatically?
How can human testers improve AI-assisted testing accuracy?
How can organizations effectively train and upskill their existing human testing workforce to maximize the benefits of agentic testing?
What unique strengths do human testers bring that AI agents cannot replicate?
How do human testers handle ambiguous AI test results?
How can agentic testing agents make autonomous decisions about test design, execution, and defect prioritization?
What skills should senior engineers cultivate to identify and mitigate AI risks, including bias, security vulnerabilities, and ethical concerns?
How do we define the “judgment call” that only humans can make in testing?
Can an AI testing agent really replicate the intuition or domain knowledge of a seasoned QA engineer?
What unique cognitive or creative skills do humans bring that machines still can’t replicate in testing?
How do you measure the impact and value of agentic testing within a QA strategy?
What are the challenges in training or developing skilled agentic testers?
What skills should testers focus on today to strengthen their agentic role in QA?
With AI becoming more agent-like itself, how do you see the boundaries shifting between “agentic testing” and AI-driven testing?
What more interesting to understand is say if the testing involves hardware , like involves camera to validate data, then how ill AgentiC CI be helpful here?
How realistic is it today to use goal-driven systems that operate on “missions” instead of scripts?