Agentic software testing for engineering and QA

Test every change
like it’s going to space.

Demand zero bugs. Put an AI testing workhorse on the hunt. Run deep checks around the clock, hammer edge cases and challenge the tests themselves.

Request a walkthrough See sample results

You set the quality bar. Your team decides what ships.

SEE WHAT THE TESTS FOUND Click through the sample workspace

QUALITY REVIEW

Review the results together.

19 recorded task outcomes

Tasks checked6in this scenario

Passed1task

Needs attention5failed tasks

Test results

4 of 6 tasks

Measured results for Shipping fault. Passed and failed counts are native check counts.
Test method	Result	Passed	Failed
Unit testsNode.js	Failed	3	1
API checksNative HTTP	Failed	2	1
Property checksCombinatorial	Failed	0	1
Property checksfast-check	Passed	1	0

What the evidence shows

The shipping boundary is wrong.

The fast-check task passes, while unit, API, combinatorial property, browser and Gherkin checks expose the deliberately seeded shipping fault.

scenario: shipping-boundaryExplore the full report

Five checks expose the shipping fault.

A website demo using real, repository-owned results. The sample ran locally, without AI or cloud execution.

Bring the tools your team already uses.

GitHubAzure DevOpsPlaywrightREST APIMCP

Set an unreasonable quality bar

Put your code through hell.
Then test the tests.

Make the software earn your confidence. TestAgent puts repeat runs, edge cases and test-suite weaknesses on the same agenda, with evidence you can inspect.

01 PUT TESTING ON THE NIGHT SHIFT

Keep testing after
your team logs off.

Give the agent a standing test plan. Schedule repeat runs and watch selected branches for changes. Eligible checks start automatically within your approved scope and budget.

Run approved checks on a schedule.
Start testing when watched code changes.
Keep the findings ready for your review.

Explore the workflow

Your standing test planWORKFLOW ILLUSTRATION

New code Schedule Test plan

Your AI quality lead

Queues eligible testing runs

Approved scope

Run within approved limits

Retain findings and evidence

Repeat when the next run is due

Your standing plan keeps the work moving.

02 GO DEEPER THAN A PASS

Make passing tests
prove themselves.

A green tick is an invitation to dig deeper. Hammer business rules with generated inputs. Deliberately alter the code and see whether the tests notice. Keep unresolved weaknesses in view.

Explore edge cases with property-based tests.
Find weak assertions with mutation testing.
Keep surviving mutations visible.

See how mutation testing works

Challenge the test suiteOWNED LOCAL SAMPLE

6survived

12 CONTROLLED MUTATIONS

A passing suite can
still have blind spots.

Six changes survived the sample tests.

6 killed6 survivedInspect the results

03 MAKE THE NEXT DECISION

Demand evidence.
Make the call.

Set the expected behaviour, then inspect what actually happened. Trace results to the tested revision and retained artifacts. Reproduce supported failures and review regression drafts before the next run.

Separate passed, failed and incomplete checks.
Inspect the original evidence.
Keep release decisions with your team.

Explore a sample report

Inspect a findingSAMPLE EVIDENCE

SHIPPING-BOUNDARY SCENARIO

The shipping checks disagree.

API checksFailed

Property checks · fast-checkPassed

Expected behaviourIndependent specification

EvidenceRetained native reports

Results.json Evidence pack

One investigation. More ways to test.

Attack the problem
from seven directions.

Browser journeys, API boundaries, business rules and the strength of your tests. One agent coordinates the methods in your approved plan.

Computer-use exploration

Investigate an approved browser journey and retain the evidence from what happened.

Explore method 02

Unit testing

Run supported unit suites against the exact source revision you are reviewing.

Explore method 03

API testing

Test responses, contracts and approved access boundaries with explicit expectations.

Explore method 04

Playwright testing

Run browser checks and inspect the retained results of supported journeys.

Explore method 05

Gherkin testing

Connect agreed Given, When, Then behaviour to executable checks.

Explore method 06

Property-based testing

Challenge rules with generated inputs and combinations beyond individual examples.

Explore method 07

Mutation testing

Introduce controlled code changes and see which ones your tests fail to catch.

Explore method

LET’S TALK ABOUT YOUR STACK

Build an evaluation around your team.

We’ll confirm supported tools, runner availability and scope with you.

Request a walkthrough

Proof you can inspect

See what the
sample tests found.

Our invoice sample includes deliberate shipping and tenant-isolation faults. See which methods caught them and where the tests still fell short.

Explore the sample report

19recorded task executions

9↗passed

10↗failed

6 of 12 mutations survived.
Unresolved survivors remain part of the report.

Download the original evidence

Owned local educational sample. No customer data, cloud execution or model-generated results.

Fits the work in front of you

Give your toughest changes
a relentless test team.

Review your next pull request.

Bind checks to the change, review the findings and make the next decision with the evidence in view.

Explore pull request testing

Challenge AI-generated code.

Test against independently reviewed requirements, so code and tests do not repeat the same mistaken assumption.

Explore the testing approach

Plan a focused evaluation.

Start with one repository or release. Agree the methods, execution boundaries and outcomes to review.

Discuss your evaluation

Get to know the platform

Questions before
your first run?

Read the product facts

01Can the AI testing team keep working overnight?

Yes. Set a schedule or watch selected branches to start approved checks automatically. Your configured runner, approved scope and spending limit govern each run. If setup, approval or allowance blocks testing, that stays visible for your team to resolve. Execution availability and support terms are confirmed during evaluation.

02What is an AI testing agent?

An AI testing agent helps plan and investigate software checks using your requirements and tools. This product coordinates an approved plan across multiple testing methods and keeps the results, evidence and unfinished checks available for human review.

03How does it work with our existing tests?

Repository inspection discovers supported existing suites and proposes a setup for review. Your team chooses the revision, requirements, methods and execution scope before approving a run. GitHub and Azure DevOps connectors are implemented; supported stacks and live runner availability are confirmed during evaluation.

04Can it test AI-generated code?

Yes. The testing workflow can investigate changes regardless of who wrote them. Its expectations should come from independently reviewed requirements, so generated code and generated tests do not simply repeat the same mistaken assumption.

05Does an agent decide when we release?

Your team makes the release decision. Passing, failing and incomplete checks remain distinct, and reports retain the findings and evidence gaps that matter to that decision.

06How can we evaluate it?

Request a walkthrough to discuss one repository or release, the expected behaviour and the methods you need. The team will confirm the supported setup, qualified execution environment, data handling and commercial terms before an evaluation starts.

07What does it cost?

Pricing is agreed for the scope of an evaluation. The walkthrough request carries no purchase commitment. Scope, run allowances and commercial terms are confirmed before paid work begins.

Meet your AI testing workhorse

Bring your toughest code.
Set your highest bar.

See how the AI testing team would challenge your next release. We’ll scope the methods, automation and evidence around your software.