Systems | Development | Analytics | API | Testing

AI now writes a growing share of your code, and examiners expect the same proof of testing no matter who wrote it. Here is what the verification gap costs a regulated institution once it surfaces in an audit.

Your manual test library is more than documentation. It contains years of decisions about what your product should do, and how to tell when it doesn't. That knowledge is a valuable starting point for automation, not something you should have to recreate from scratch. The clearest example came up in a customer conversation. The head of quality at a fast-growing fintech told me his company pays an outside firm to run its manual regression, with a bill for every test executed.

‍ A digital front door strategy coordinates patient access, engagement, and supporting health system workflows. It connects provider search, scheduling, registration, payments, and follow-up through integrated digital services. Effective digital front door (DFD) implementation requires EHR integration, accessible interfaces, secure data exchange, and clear operational ownership.

‍ A digital front door platform connects patient-facing services with clinical and administrative workflows. Capabilities can include appointment scheduling, digital intake, patient communication, insurance verification, and payments. Coverage varies by vendor, product configuration, and supported electronic health record (EHR) integrations. Evaluating a digital front door (DFD) solution should cover complete workflows, integration depth, usability, security, and total operating costs.

In one release test, my AI agent triaged 43 backlog items into 134 executable cases in Katalon True Platform. The first pass at the steps caused trouble. We had bundled setup, navigation, action, and judgment into long instructions. The runner returned verdicts, but a failed step left me unsure which expectation it had checked. We split the cases into one action and one checkable expected result per step. The failures became specific enough to review against the screenshots and session recording.

“Customers can apply a discount code” looks ready for testing. Give that sentence to an AI model and it can produce cases for valid, invalid, expired, and reused codes in seconds. The trouble starts when someone asks what a valid code should do. Which basket is eligible? Does the discount change the displayed total before or after tax? Should the saved order carry the same amount?

As part of my work, I regularly consult with teams comparing Unreal Engine version control performance and weighing whether Perforce P4 is the right fit for their projects. Based on these conversations, I decided to develop a repeatable framework that others can use to objectively benchmark P4 against other version control systems.