Systems | Development | Analytics | API | Testing

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.

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defect counts in the bug tracker, and 40 minutes later delivers a hand-built status summary that is already slightly out of date.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.

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.

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.

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.

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.

Most AI tools for business data sound authoritative even when they are wrong. The problem is not the model. It is the architecture behind it.

Hybrid AI systems need secure ways to manage user identities across cloud and on-premises environments. Identity passthrough ensures that AI systems operate under the permissions of the actual user, not a shared service account. This approach reduces risks tied to credential theft, improves auditability, and supports compliance with regulations like GDPR and HIPAA. Key methods for identity passthrough include: Quick Takeaway: For organizations prioritizing simplicity, PHS is a good starting point.

We are in the middle of the fastest acceleration in software development that the industry has ever seen. Thanks to highly capable models from technology leaders like Anthropic and OpenAI, we have entered the era of vibe coding—a world where developers describe what they want in natural language and get working software in return.