AI in Testing
Agentic testing
Also known as: AI testing agents, autonomous testing
Agentic testing is the use of AI agents that plan and carry out testing tasks with limited human direction — generating tests, running them, diagnosing failures, and adapting — rather than only assisting a human one step at a time.
Read the full guide: Agentic testing, explainedThe shift is from AI as an assistant (suggest a test, summarize a failure) to AI as an agent that takes a goal — "cover the checkout flow", "find why this launch is risky" — and works through the steps itself: exploring the app, writing or selecting tests, executing them, and interpreting the results.
Agentic testing is an umbrella over capabilities that already exist in pieces: AI test generation, self-healing tests, failure clustering, and conversational analysis of results. What makes it "agentic" is the loop — the system acts, observes the outcome, and decides what to do next, instead of waiting for a human at each step.
- AI as an agent that pursues a goal, not just a step-by-step assistant.
- Spans generation, execution, self-healing, and failure diagnosis in one loop.
- Keeps a human in the loop for judgment — it removes the repetitive work before it.
Frequently asked
Does agentic testing replace QA engineers?
No. Agentic testing automates the repetitive parts of testing — generating cases, triaging failures, updating brittle tests — so engineers spend more time on judgment: deciding what to test, what risk is acceptable, and whether a failure matters. It changes the work, it does not remove the need for it.
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See it in your own test results
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Last reviewed June 11, 2026