AI test creation
AI-powered test creation for web applications
Describe the flow in plain English, or point Klarent at a user story or design file. The agent writes the executable test, and you review it like any other change.
The test authoring problem
Why test authoring is the bottleneck
Coverage capped by scripting speed
Automation coverage grows only as fast as engineers can write and debug scripts, and there are never enough hours for the backlog.
Knowledge without the tooling
Manual testers hold the deepest product knowledge, but cannot write the automation that would put it to work on every build.
A widening coverage gap
Every new feature widens the gap between what ships and what is covered, so regression risk quietly compounds release after release.
How it works
How AI test creation works
Step 1 - Input
Start from a plain-English description, a user story, a design file, or a recorded session of the flow.
Step 2 - Generate
The AI agent works through the live app step by step, producing an executable test with steps and assertions.
Step 3 - Review and run
A human reviews the generated test before it enters the suite, then it runs on demand, on a schedule, or from CI/CD.
Platform capabilities
Automated test generation from the inputs your team already has
Plain-English authoring
Write natural language test instructions. No scripting language to learn, no locator strategy to design.
From user stories and tickets
Turn a user story or Jira ticket into a test case, so acceptance criteria become checks that run on every build.
From design files and PRDs
Generate tests from a Figma link, PRD or spec sheet, so coverage lines up with what the product is meant to do.
From a recorded walkthrough
Walk through the app once, and the agent turns the recording into a readable, editable test.
Edge cases and negative paths
The agent drafts edge cases and negative paths alongside the happy path, for you to accept or reject in one click.
Coverage gap detection
See what is untested against what shipped, and generate the missing tests from there.
What the agent produces
Tests your whole team can read
Readable test steps
Plain English test steps that a non-engineer can review and sign off.
Business-level assertions
Checks tied to business outcomes, not element IDs.
Test data variations
Variables and secrets that run the same flow with different data.
Executable Playwright code
Code behind every step, which you can export and run anywhere.
Who can author tests now
Test authoring for the whole team, not just SDETs
Manual QA, analysts and product owners
Anyone who can describe how the product should behave can author a test, turning product knowledge directly into automated coverage.
Engineering stays in control
Generated tests go through review and approval before they join the suite, so quality control stays in engineering hands.
Klarent vs. traditional QA
AI test creation vs record-and-playback vs scripting
| Scripting | Record-and-playback | Klarent AI test creation | |
|---|---|---|---|
| Authoring speed | Slowest. Every step, locator and wait is written by hand | Fast to record, slow to clean up into something reusable | Describe the flow and the agent writes the test |
| Who can do it | SDETs and developers | Anyone can record, but engineers usually fix the output | Anyone who can describe the flow in plain English |
| Readability | Code, readable only to engineers | Raw clicks and selectors, hard to follow | Named steps in plain English, backed by code |
| Maintenance load | High. Scripts need manual fixes after UI changes | High. Recordings often need re-recording after UI changes | Self-healing proposes fixes for review |
| Fragility | Breaks when selectors or timing change | Tied to the exact page structure it was recorded on | Steps are verified against the live app as they are generated |
Generate your first test from a user story.
Bring a user story from your backlog and see Klarent turn it into an executable, reviewable web test in a live walkthrough.
FAQs
You describe the flow in plain English, or start from an input like a user story or PRD. The AI agent opens your app and builds the test one step at a time: it reads the current state of the page, writes the next step and the code to run it, executes that code, and checks a screenshot against the expected outcome before moving on. It repeats until the instructions are fulfilled.
Every step is executed and verified against the live application while the test is being generated, so a step is only kept once it actually works. If a step fails, the agent reworks and re-runs it. A human then reviews the finished test before it joins the suite.
Yes, and that is by design. Generated tests and suggested scenarios are reviewed and accepted before they enter the suite, so engineering keeps control of what runs.
Plain-English instructions, user stories and Jira tickets, PRDs and spec sheets, Figma links, website URLs, existing tests, and recorded walkthroughs of the app.
Yes. Manual QA engineers, business analysts and product owners can write tests as plain English test steps, with no scripting or locators involved. Review and approval keep quality control with engineering.
Yes. Edit the instruction on any step, and the agent regenerates the test from that point, leaving earlier steps untouched. You can also correct a test while it is still generating.
Record and playback captures raw clicks tied to the exact page structure it was recorded on, so recordings break when the UI changes. Klarent produces named, readable steps with assertions, verifies each one against the live app, and self-heals when the UI changes.
Yes. The agent generates each step against the application's current state rather than a fixed script, and reworks steps that fail. Browser states, reusable modules, variables and secrets handle logins, shared setup and data-driven flows.