Systems | Development | Analytics | API | Testing

Gherkin's structured, human-readable format gives it a decisive edge when working with AI-powered testing tools. Start evaluating your test suite structure now, as AI-powered QA is becoming the industry standard, and your test format determines how well these tools can assist you. The debate over Gherkin vs traditional testing has taken an unexpected turn.

AI-powered analytics is everywhere right now. But the payoff? Not so much. Two patterns show up again and again. The first is an “AI everything” backlog that expands faster than teams can deliver. The second is an insights bottleneck that still forces the business to wait in line for basic answers while analysts drown in ad hoc requests.

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.

Kong Agent Gateway Is Here — And It Completes the AI Data Path You had a request going to a model, a response coming back, and a gateway in between to enforce policy. With the right solutions, this becomes manageable pretty quickly.. That world is over. Today's agentic architectures look nothing like that. Agents are delegating tasks to other agents via A2A. These other agents are producing and consuming event streams.

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.