Systems | Development | Analytics | API | Testing

Software teams are under constant pressure to release faster. Yet testing, the safeguard that protects quality, has not kept pace with modern delivery speeds. More code and shorter sprints overwhelm QA capacity, while fragmented tools and late-stage performance checks create bottlenecks that slow everything down. The question is not whether testing needs to evolve. The question is how to evolve without a costly rip-and-replace of your existing stack.

AI has opened the door for teams to build tools they previously had to buy. With the right prompts and internal workflows, teams can generate test cases, summarize results, analyze defects, and automate parts of the testing process faster than ever. For enterprise QA and engineering leaders, that raises a practical question: “should we build our own test management layer, or adopt an AI-powered test management platform?” It’s a fair conversation to have.

If you only follow the loudest headlines, it is easy to believe AI is about to wipe out manual testing. Katalon's State of Software Quality Report 2025 tells a more useful story, and it comes from inside the industry: over 1,500 QA professionals, from individual contributors to senior executives, across North America, Europe, and Asia-Pacific.

The state of neobanking 2026 looks very different now. A few years ago, most digital banks were chasing growth at any cost. More users. More app downloads. More market buzz. Today, the focus has shifted. Investors want profitable business models. Regulators want tighter compliance. Customers expect their neobank to feel as reliable as a traditional bank, but far more seamless to use. The industry is finally moving from hype toward operational maturity.

If your team spends more hours fixing tests than writing features, you already know the problem. You don’t need another lecture on "why testing matters." You need to reduce test maintenance without gutting your coverage, and you need it to actually stick past next sprint.

AI Transport v0.5.0 is now available. It adds first-class support for running an agent turn inside a durable execution framework, such as Temporal or Vercel's Workflow Development Kit (WDK), while every client watching the conversation still sees one clean, resumable stream. The last release, v0.4.0, let an agent hydrate its history from your own database. This one is about what happens when the process running the agent isn't around for the whole turn.

What an incredible day at the Agentic Analytics Playbook EMEA event! It might have been baking hot outside, but inside the Nobu Hotel, the energy was absolutely electric.

Test execution is one of the most time-sensitive stages of software delivery. Teams are expected to validate functionality, ensure stability, and support release decisions within increasingly shorter development cycles. Even with strong automation in place, there is rarely enough time to execute every Test before a release. This makes prioritization a critical part of the QA process.