# TestAgent > An agentic AI testing workhorse for engineering and QA. Schedule deep checks, test code changes and challenge passing tests across seven methods. TestAgent is a working name. The original repository and sample use the internal label Tenhaw Quality. The final product brand is undecided. The operator is Tenhaw LTD, company 12735685, England and Wales. ## Product and access Engineering and QA teams use the workflow to investigate changes, approve testing scope and review findings with retained evidence. Humans approve the plan and decide what ships. Evaluations are arranged with the team; supported stacks, live provider qualification, data terms and prices must be confirmed. ## Testing methods - Computer-use exploration: Investigate an approved browser journey and retain the evidence from what happened. - Unit testing: Run supported unit suites against the exact source revision you are reviewing. - API testing: Test responses, contracts and approved access boundaries with explicit expectations. - Playwright testing: Run browser checks and inspect the retained results of supported journeys. - Gherkin testing: Connect agreed Given, When, Then behaviour to executable checks. - Property-based testing: Challenge rules with generated inputs and combinations beyond individual examples. - Mutation testing: Introduce controlled code changes and see which ones your tests fail to catch. ## Pages - [Relentless AI software testing for every code change](https://cueapril.com/): An agentic AI testing workhorse for engineering and QA. Schedule deep checks, test code changes and challenge passing tests across seven methods. - [How the testing workflow works](https://cueapril.com/how-it-works): Select the source revision, review the test plan, approve scope and inspect evidence. The team retains release decisions. - [Seven software testing methods](https://cueapril.com/testing): Computer-use exploration, Unit testing, API testing, Playwright testing, Gherkin testing, Property-based testing, Mutation testing - [Owned local sample evidence](https://cueapril.com/sample-report): 19 local task executions, 9 passed, 10 failed and 6 mutation survivors. Deliberately seeded faults, without cloud, model or customer execution. - [Software testing resources](https://cueapril.com/resources): Practical guides to testing agents, generated code and pull request workflows. - [Product facts](https://cueapril.com/product-facts): Implemented capabilities, approval controls and evaluation requirements. Qualified execution, supported stacks and commercial terms are confirmed with the team. - [Request a walkthrough](https://cueapril.com/demo): Request a conversation about the workflow and evaluation scope. This does not create a product account or approve paid work. - [Privacy notice](https://cueapril.com/privacy): Tenhaw LTD operates the site. Enquiries are stored on Railway for 90 days. Vercel hosts the website. Separate privacy requests do not require sales consent. - [Find the weaknesses in your passing tests.](https://cueapril.com/testing/mutation-testing): Use mutation testing to see which deliberate code changes your tests detect. Inspect native Stryker results and unresolved survivors. - [Test the rules across more inputs.](https://cueapril.com/testing/property-based-testing): Explore approved business rules with property-based testing, bounded input generation, recorded counterexamples and reproducible replay. - [Check what your API actually returns.](https://cueapril.com/testing/api-testing): Run approved API assertions and supported schema campaigns. Inspect response checks, failed cases and the exact scope behind each result. - [Check the journeys your users rely on.](https://cueapril.com/testing/playwright-testing): Run approved Playwright browser tests with retained results, source identity and visible gaps. Review supported accessibility scans alongside journeys. - [Put your existing unit tests to work.](https://cueapril.com/testing/unit-testing): Run supported unit suites against a pinned repository revision and inspect native results. Start with existing tests and review the broader testing scope. - [Check the behaviour your team agreed.](https://cueapril.com/testing/gherkin-testing): Execute approved Gherkin scenarios with Cucumber and retain scenario-level outcomes, native reports and visible incomplete steps. - [Investigate how your application behaves.](https://cueapril.com/testing/exploratory-testing): Use computer-use exploration within approved browser actions and expectations. Inspect observations and verify supported findings in fresh execution. - [Give a quality agent a clear testing job.](https://cueapril.com/guides/agentic-software-testing): Understand agentic software testing: approved intent, coordinated test methods, reproducible findings and evidence for a human release decision. - [Check AI-generated code against your requirements.](https://cueapril.com/guides/testing-ai-generated-code): Review AI-generated changes with approved expectations, multiple testing methods, mutation checks and inspectable execution evidence. - [Bring testing evidence to your pull request.](https://cueapril.com/use-cases/pull-request-testing): Connect approved testing to GitHub pull requests. Review tested revisions, actual outcomes, findings and outstanding scope before deciding to merge. ## Evidence - [Raw sample evidence](https://cueapril.com/evidence/sample-evidence.json): 19 owned local task executions, 9 passed, 10 failed. Twelve mutations, with six survivors. No model or cloud execution and no customer data. - [Evidence pack](https://cueapril.com/evidence/sample-evidence.zip): original specification, source, examples, selected reports and file checksums. - [Evaluation guide](https://cueapril.com/evidence/evaluation-guide.md): setup, execution boundaries, export and evaluation questions. ## Common questions ### Can 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. ### What 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. ### How 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. ### Can 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. ### Does 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. ### How 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. ### What 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. ## Machine-readable reference - [Full guide corpus](https://cueapril.com/llms-full.txt) - [Sitemap](https://cueapril.com/sitemap.xml) Content reviewed: 2026-09-25. These files are convenience references, not an API or authorisation to execute a test.