How do you automate smoke testing with AI?
A smoke test is a fast check that your most critical paths still work after a deploy. With AI you curate 5–20 happy-path goals into a smoke plan and an agent runs them in minutes against any environment, returning a clear pass/fail before users are affected.
Sub-use-cases
Covers Post-deploy smoke check, pre-release gate, daily production heartbeat and per-environment smoke runs.
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What a smoke test should cover
Only the make-or-break paths: login, the core transaction, the key dashboard, a critical API-backed action – enough to know the build is alive.
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How does an AI smoke test run?
You tag a small set of critical tests as a smoke plan; it runs in parallel on every deploy or on a schedule, and reports pass/fail fast enough to gate a release.
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When smoke tests fire
On every deploy and daily on production – when a full regression run is too slow to do that often.
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Who runs smoke testing
Teams deploying frequently who need a fast release gate.
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How QA.tech helps
QA.tech makes the smoke plan a one-click, parallel, minutes-long check, so a broken critical path is caught before customers hit it – not via a support ticket. Smoke runs use the same AI QA testing engine as your full suite.
Companies running smoke testing with QA.tech
FAQ
Common questions
- How is smoke testing different from regression testing?
- Smoke is a fast check of a few critical paths; regression re-verifies the full feature set. Run smoke on every deploy, regression on a schedule.
- How fast is a smoke run?
- Minutes – the curated set runs in parallel.
- Can QA.tech gate a deployment?
- Yes – wire it as a blocking check so a failed smoke test halts the release.
Related use cases
Automated 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.
ReadExploratory Testing
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.
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
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