“Stories change faster than our test cases.”
Requirements, specs and existing cases live in different tools. Turning them into reviewed scenarios is manual, slow and rarely complete.
Agentic Quality Engineering
From test automation
From test automation
to quality automation.
TuringOne takes a ticket marked ready to test and carries it all the way to a documented release decision. Agents design, automate, execute, analyse and maintain your tests inside your own tools. Your QA team keeps the two decisions that matter.
For teams asked to ship faster without lowering the bar.
PAY-1428
Add a beneficiary to a transfer
- 01Design14 scenarios from story, specs and existing casesDone
- G1QA approves casesHuman gate, before any automationApproved
- 02AutomationExecutable tests in your framework, data readyDone
- 03ExecutionYour pipeline · 1 locator repaired, assertions untouchedHealed 0.94
- 04Analysis1 failure qualified on the app side, ticket draftedDone
- G2QA lead decides releaseReport, indicators, open risksAwaiting you
13/14Scenarios passed
1Qualified defect
2Human decisions
Runs in your tools and pipeline
Human gates by design
EU or in-house AI models
Data anonymised before every AI call
Every action traceable
The problem
Your team doesn't lack scripts. It lacks hours.
Generating test code got cheap. Everything around it didn't. That's where QA teams lose their sprints, and where release confidence quietly erodes.
“The automation backlog never shrinks.”
Every sprint adds regression scope faster than the team can automate it. Test data has to be prepared by hand, again.
“Half our time goes into fixing tests.”
A renamed button or a new API version breaks dozens of scripts. Maintenance eats the capacity meant for new coverage.
“Red pipeline. Is it the test or the app?”
Failure triage takes hours of log reading before anyone can say whether there is a real defect to open.
“Can we release?” “Probably.”
Release decisions are made on partial dashboards and gut feeling, with little trace of what was checked and why.
“AI tools? Not with our data.”
Teams can't send sensitive data to a black box, and can't accept a tool that rewrites assertions to turn tests green.
The shift
The challenge isn't generating scripts.
The challenge isn't generating scripts.
It's running a quality ecosystem.
TuringOne doesn't add another script generator to your stack. It automates the whole quality chain around your existing tools, and leaves the judgement calls with your people.
What mattersClassic test automationQuality automation with TuringOne
Starting point
Classic test automation: A tester writes the script
TuringOne: The ticket, its specs, existing cases and data structures
Test design
Classic test automation: Manual, often incomplete
TuringOne: Scenarios generated from requirements, approved by QA
Test data
Classic test automation: Prepared by hand each cycle
TuringOne: Datasets generated automatically
Maintenance
Classic test automation: Broken locators fixed one by one
TuringOne: Scoped self-healing, assertions never touched
Failure triage
Classic test automation: Hours of log reading
TuringOne: Each failure qualified (test or application) with a drafted ticket
Release decision
Classic test automation: “Looks green enough”
TuringOne: Quality report, indicators and a documented decision by your QA lead
Dependency
Classic test automation: Proprietary runner
TuringOne: Tests live in your framework and keep running without the agents
How it works
One chain. Four autonomous stages. Two human gates.
From the moment a ticket is ready to test to the moment someone signs off the release.
01
Agents
Design
Scenarios from stories, specs and existing cases.
Gate 1 · You
Approve
QA approves cases before anything is automated.
02
Agents
Automate
Executable tests in your framework, data ready.
03
Agents
Execute
UI, API, end-to-end, regression in your pipeline.
04
Agents
Analyse
Each failure qualified: fix the test or open a ticket.
Gate 2 · You
Decide
The QA lead decides on the release report.
Bounded quality loop: self-heal, replay, revalidate. At the limit, the run escalates to a human. It never loops silently.
What matters to you
Three priorities. One chain.
Depending on what you are accountable for, TuringOne answers a different question.
Get the sprint back, and keep the final word.
If you care about:
Agents do the repetitive work. Your team approves the cases and decides the release.
Explore this priority- Less writing, more reviewing. Scenarios arrive drafted from the ticket; the team reviews instead of starting from a blank page.
- Maintenance that stops eating the sprint. Locators and endpoints are repaired in scope; you review a small change, not a broken suite.
- Triage in minutes, not hours. Every failure arrives qualified, test side or application side, with evidence.
- A release call you can defend. A report with indicators and open risks, and the decision on record.
- No new tool to learn. Tests land in your framework, run in your pipeline, tickets go to your tracker.
Scale quality across teams without scaling headcount.
If you care about:
One governed way of working, measured with the same indicators in every team.
Explore this priority- A standard chain, not team-by-team scripts. Same stages, same gates, same evidence everywhere.
- Indicators you can report upward. Time to automate, regression coverage, triage time, healing precision, false positives.
- Faster feedback for product. Ready-to-test tickets reach a release decision with less waiting.
- Governance built in. Human validation, full traceability, versioned instructions and rollback per agent.
- Adoption without disruption. Works with the frameworks and pipelines your teams already run.
Ship faster with a risk profile you can defend.
If you care about:
Measured before and after on your own scope, not on a vendor benchmark.
Explore this priority- Cost per test you can see. Cost and response time tracked per test, next to QA hours saved and hours spent reviewing.
- No lock-in. Generated tests run without the agents; you can stop at any time and keep everything.
- Data stays protected. Read-only access to sources, anonymisation before every AI call, EU or in-house models.
- Fits your environment. Plugs into the delivery tools and pipelines you already use.
- Low-risk start. A 1 to 3 week pilot on one scope, with a before/after report.
Self-healing, with a hard line
Locators may change.
Locators may change.
Assertions never.
Most tools over-promise here. A repaired test must never be able to hide a regression, so the agent can only touch how the test reaches the application, never what it must verify.
- Failure qualified on the test side first, application behaviour unchanged
- Scoped change with a confidence score, replayed separately
- Low score goes straight to a tester; the main suite only changes after review
# heal · add_beneficiary · confidence 0.94
- page.click("#btn-add-beneficiary")
+ page.click("[data-testid='beneficiary-add']")
expect(rows).toHaveCount(3) # locked
expect(toast).toHaveText("Added") # locked
# replayed ✓ · awaiting review
Illustrative repair: a button identifier changed, the locator was repaired, assertions stayed exactly as written.
Measured, not promised
We measure your numbers first. Then we measure the difference.
No vendor benchmark. Every pilot starts by measuring these indicators on your scope, and ends with the same measurement.
Time to an automated test
From ready-to-test ticket to an executable test in your framework.
Regression automated
Share of your regression scope running automatically.
Failure qualification time
How fast a red result becomes “fix the test” or “open a ticket”.
Data preparation effort
Hours spent building test data per cycle.
First-run success rate
Generated tests that run on the first attempt.
Healing precision
Correct repairs divided by repairs proposed.
False positives & agreement
How often the automatic evaluation and your reviewers agree.
Cost & hours
Cost per test, QA hours saved versus hours spent reviewing.
Governance
Agents execute.
Agents execute.
Humans govern.
Governance is designed into the chain, not added afterwards. It's what lets teams trust agents with their quality process.
Governance in detailTwo human gates
QA approves cases. The QA lead decides the release. The chain stops and waits.
Protected data
Read-only on sources, anonymised before every AI call, secrets never shown to an agent.
Every output evaluated
An automatic evaluation sorts the review queue, least confident first, so reviews never become rubber stamps.
Traceable & reversible
Every run, action and decision recorded. Any agent can be rolled back.
Next step
See it on your own application, not on a demo.
One end-to-end scope, 10 to 20 scenarios reviewed with your team, 1 to 3 weeks, indicators measured before and after. Three decisions are enough to start.