# How to Automate Smoke Testing with AI

> Automate smoke testing with AI agents – a fast, curated check of your most critical paths that runs in minutes after every deploy.

Source: https://qa.tech/use-cases/smoke-testing

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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](https://docs.qa.tech/core-concepts/test-plans) and an agent runs them in minutes against any environment, returning a clear pass/fail before users are affected.

[Book a demo](https://qa.tech/demo)

Sub-use-cases

**Covers** Post-deploy smoke check, pre-release gate, daily production heartbeat and per-environment smoke runs.

1.  01
    
    ## 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.
    
2.  02
    
    ## How does an AI smoke test run?
    
    You tag a small set of critical tests as a [smoke plan](https://docs.qa.tech/core-concepts/test-plans); it runs in parallel on every deploy or on a schedule, and reports pass/fail fast enough to gate a release.
    
3.  03
    
    ## When smoke tests fire
    
    On every deploy and daily on production – when a full regression run is too slow to do that often.
    
4.  04
    
    ## Who runs smoke testing
    
    Teams deploying frequently who need a fast release gate.
    
5.  05
    
    ## 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](https://qa.tech/ai-qa-testing) engine as your full suite.
    

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## Companies running smoke testing with QA.tech

-   [![Upsales](https://qa.tech/assets/upsales-CwvOOTpd.svg)Runs daily automated smoke tests that used to be triggered by hand.](https://qa.tech/case-studies/how-upsales-replaced-320h-of-manual-testing-with-agents)
-   [Runs continuous smoke tests so regressions surface early, not after release.](https://qa.tech/case-studies/how-pricer-transform-qa-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](https://docs.qa.tech/configuration/ci-cd-integration) so a failed smoke test halts the release.

Related use cases

-   [
    
    API Regression Testing
    
    You group API contract checks into a plan written as plain-language goals – "GET /orders returns 200 with a list, and every item has an id and a status" – and an AI agent runs them against your API environment on every deploy. The agent makes the requests, writes and executes its own validation code, and returns a verdict with the full request and command trace.
    
    Read ▒ ▓ ▓ █░░░ █ ▒ ▒░▓░ ▒▓░ ░░▒░▓░▒▒▓░▓░░ ▒░ ▒ ▓ ░ ░▒ ░░░ ░▒▒▓░░ █░░░░ ▓░░▒░░ ▒▒ █░ ░ ░▒ ░░ ▒ █░ ▒░░░█░ ░ ▓▒▓▓ ▓](https://qa.tech/use-cases/api-regression-testing)
-   [
    
    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. Many teams wire this into PR testing so the suite runs on every pull request preview. 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. This is the workflow behind our automated web testing product.
    
    Read ░ ▒░░▒░░░░ ▓▒▓░ ▓░░ ░░▒░░█ ░▒░ ░▒░▓░ ░ ░ ░▒ █ ░ ░ ░█▒▓ ░ ▓ ░█ ░ ░░ ▓░▒ ░▒▓ ░░░░░▓░ ░░░ ▓░ █ ░░░ ▒░░▓▒▒░ ▒░▒░ ▒▒▒](https://qa.tech/use-cases/automated-regression-testing)
-   [
    
    Exploratory 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.
    
    Read ░█░▒ ░▓▒░▒ ░░ ▒░▒ ▒▒▒▓ ▒░░ ░▓░ ░▒ ▒ ▓█▓░░░▒░░░ ░▓ ▒░░░ ▒▒░░░░░ ▒ ░ ▒▒░ ░░░▒▒ █░░▒░ ░░ ░ ░░ ░░ ░ ░░ ░ ▒](https://qa.tech/use-cases/exploratory-testing)

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