What is exploratory testing, and can AI do it?
Exploratory testing means investigating an application to discover how it behaves and where it breaks, rather than following a fixed script. AI can do this: an agent clicks through a new or changed feature, finds the flows and edge cases, and proposes test cases for what it sees. With QA.tech this runs automatically when a feature lands.
Sub-use-cases
Covers Exploration of a new feature on a PR, discovery of untested areas, auto-generation of candidate cases, edge-case surfacing and automated exploratory testing in agile sprints.
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What exploratory testing uncovers
Newly built features with no existing tests, edge cases beyond the happy path, and changed areas where behaviour may have shifted unexpectedly.
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How does an AI agent explore a new feature?
When a PR is opened or an area flagged, the agent crawls just the changed part of the app, identifies new patterns, and generates concrete cases (e.g., "a dark-mode toggle was added – verify it activates, persists on reload, and is consistent across pages"), which you review and add to a plan.
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When to reach for exploratory testing
The moment a new feature lands, before formal cases exist – and whenever you suspect untested risk.
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Who uses automated exploration
QA engineers and developers who need new features tested immediately, without designing cases from scratch.
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How QA.tech helps
Writing cases for brand-new features is the slowest part of growing coverage. QA.tech does the exploration and proposes the cases, turning a manual bottleneck into a reviewable starting point.
Companies running exploratory testing with QA.tech
FAQ
Common questions
- What's the difference between exploratory and scripted testing?
- Scripted testing re-runs known cases; exploratory discovers unknown behaviour and new cases. QA.tech automates the discovery and hands you cases to keep.
- Does QA.tech explore the whole app or just changes?
- For a PR it focuses on the changed area; on demand it can map the whole application.
- Are the generated tests usable as-is?
- They come with goals and steps ready to review and promote into a regression plan.
Related use cases
AI Test Generation
QA.tech generates tests five ways, so coverage can grow from wherever your team already works: a plain-language description, a crawl of your app, an issue tracker, your pull requests, or the API. You review and approve rather than author from scratch, which removes the slowest step in growing coverage.
ReadAutomated Regression Testing
Regression testing re-checks that existing features still work after a change. To automate it with AI, you group tests into a regression plan written as plain-language goals, and agents run the whole suite in parallel on every deploy. A 50-test suite that took hours by hand finishes in around ten minutes, and the tests don't need rewriting when the UI shifts.
ReadMulti-Environment Testing
You point the same test plan at staging, pre-prod, production or a preview URL, and the agent runs identical goals against each. Divergent results between environments surface the config and data differences that cause "it worked in staging" – before users hit them.
Read
End-to-End Testing
Next →Feature Flag & A/B Test Testing
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