A quality lead that shows its work

Go from a code change
to evidence you can use.

Give the agent the context. Agree the plan. Inspect what the tests actually found, with your team in control of the release.

01 / Give the agent your context

Start with the change you need to understand.

Choose an exact source revision from a connected GitHub or Azure DevOps repository. Bring the requirements, standards and expected behaviour your team has agreed. Repository inspection discovers supported test suites and proposes the setup needed to use them.

02 / Review the proposal

Approve the work before it runs.

Your quality lead proposes a focused investigation. Review the testing methods, targets, environment actions and execution allowance. Setup readiness is checked against the intended scope. Creating a project does not start a test or approve spending.

03 / Investigate the change

Let several methods challenge the same assumption.

Approved tasks run through the configured execution environment. The workflow can combine existing suites, API and browser checks, generated input tests and mutation testing. Generated tests use approved expectations; new assumptions need review.

04 / Inspect the findings

See what happened and what it means.

Review the source revision, expected behaviour, outcomes and retained artifacts together. Passing, failing, blocked and incomplete checks stay distinct. Supported findings can be reproduced and approved definitions can become regression drafts. A fresh run verifies what a change actually fixed.

05 / Make the decision

Keep the release decision with your team.

The report supports a release review. Your team decides which findings to resolve, which gaps need more investigation and whether the release should proceed. Set an approved schedule or watch selected branches to keep checks running within your standing scope and allowance. Repeat runs need current authority, an available runner and enough budget.

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.

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