Systems | Development | Analytics | API | Testing

Watch an AI agent run a regression test sometime. Really watch it. It lands on a login page. It reads the entire page structure: every element, every attribute, every nested div someone added in 2019 that nobody has dared to remove since. Then it reasons. It weighs its options. It explains to itself, at some length, that the button labeled "Sign in" is probably the one that signs you in. And then, finally, it clicks. Now multiply that by every step, in every test, in every regression run, every night.

Quick summary: SAP Enterprise Performance Testing (EPT) by Tricentis now integrates with SAP Cloud ALM, so teams can create, run, and track performance tests in the same hub as the rest of their S/4HANA transformation. For teams managing SAP transformations, most major workstreams are visible in Cloud ALM, but performance testing has long been an exception. It has lived in a separate tool, forcing teams to treat it as a separate workflow that’s often left to the end of a project.

Mike Dillon is a Global Platform Leader at IBM. While deeply technical and experienced in enterprise architecture, he is not a traditional hands-on software developer. Yet this spring, Dillon paired his domain expertise with IBM Bob, IBM’s AI coding assistant, to engineer his first full-scale enterprise application: WatsonAuditX. Designed to automate the documentation required for complex SAP audits, the app addresses a familiar operational roadblock.

In September, Anthropic’s CEO argued that AI companies should deliberately slow the pace at which they improve their models, because advances are outrunning researchers’ ability to understand and control them. Sam Altman endorsed the idea within hours, and other leaders of frontier AI labs quickly echoed him. For QE teams, the pace problem is already here. AI-generated change is arriving faster than anyone can review, and it isn’t arriving clean.

Your frontend is ready, but the backend endpoint it depends on ships next sprint. Your QA team wants to test a payment flow, but the third party sandbox is down again. Your CI pipeline fails twice a week because a staging database returned different data. All three problems have the same fix: an API mocking tool. API mocking tools let you simulate an API, or any dependency your service talks to, so you can build and test without waiting on the real thing. The hard part is picking one.

‍ A digital front door (DFD) connects patient-facing channels with clinical, administrative, and financial workflows. It supports appointment scheduling, registration, care navigation, and payments through integrated websites, applications, and messaging. In 2026, digital front door healthcare trends increasingly center on automating these connected workflows. Platforms such as Luma and Notable combine patient interactions with operational processes and electronic health record integration.

Effective SaaS onboarding testing depends on simulating real onboarding flows, minimizing friction, and iterating based on analytics and user feedback. To improve user retention, go beyond funnel analysis – experience the onboarding process as a new user under realistic conditions. Testing with diverse, live user journeys reveals blockers that scripted tests often miss.