Systems | Development | Analytics | API | Testing

Before your team invests in any AI testing capability, there is one question worth asking plainly: does this platform get smarter the more you use it, or does it start from scratch every single time? The term "intelligent test automation" is used generously across the industry right now. Nearly every testing tool has added AI features: auto-generated test cases, smart locator healing, suggested assertions, anomaly detection. But intelligence, in any meaningful sense, requires memory.

You asked Claude to generate a bitrise.yml. It came back clean: right steps, reasonable workflow names, valid YAML. You almost merged it. Then you noticed it’s using before_run instead of step bundles. There are no version locks on steps. The triggers are structured in a format Bitrise deprecated months ago. It’s a valid config, but it would never pass code review. The quality of an agent's interaction with your CI/CD comes down to two things: what it can do and what it knows.

Tableau Next launched as a cloud-only platform on Salesforce Hyperforce. Every generative AI capability on Tableau’s roadmap runs through Salesforce Data Cloud. But for ISVs serving healthcare, financial services, or any customer operating under regulations like GDPR, HIPAA, or DORA, this locks them out completely.

let ‘s be real, the financial side of healthcare is a mess. For patients to schedule appointments and insurers to disburse the final reimbursement, the financial process must work seamlessly. When these systems work on a disconnected workflow, delays are bound to happen. To top it all, the sheer volume of patient data doesn't make the job easier. Its not about just losing money but also about losing patients’ valued time. It is important to have a centralised system.

Picture this: it's the Thursday before a major release. The VP of Engineering asks a simple question in the planning meeting: "Are we confident we can ship Friday?" The QA lead opens four dashboards, pulls an export from the test management tool, cross-references it with execution results from a separate environment, reconciles defect counts in the bug tracker, and 40 minutes later delivers a hand-built status summary that is already slightly out of date. The team isn't slow. The team isn't incompetent.

At Qlik Connect, one of the big messages we’re putting in front of customers is this: you’re closer to agentic AI than you think. I believe that because a lot of our customers already have more of the foundation in place than they may realize. If you have been working to improve data quality, strengthen governance, connect data across the business, and move analytics beyond reporting into real decision support, you are already building the conditions agentic AI needs to deliver value.

“The data integration landscape is currently undergoing a seismic shift. We are moving away from an era of human-led data pipelines and entering the age of Agentic Data Management.

APIs are bits of software that act as interpreters for two different programs. They'll connect to each service via endpoints and relay messages back and forth, doing the work of software integration for you. DreamFactory is a secure, self-hosted enterprise data access platform that provides governed API access to any data source, connecting enterprise applications and on-prem LLMs with role-based access and identity passthrough. But how does this actually look in the real world?

Choosing the wrong date-time API can seriously snarl up your Kotlin app. Timezone mismatches, formatting bugs, inconsistent timestamps – all of them can seriously drain your time and they’re hard to trace without the right tooling. Kotlin gives you multiple date-time tools – LocalDate, Instant, DateTimeFormatter, and kotlinx-datetime – but each is designed for a specific use case across Android, server-side, and multiplatform projects.