Systems | Development | Analytics | API | Testing

Insurance underwriting automation integrates data ingestion, validation, risk scoring, decisioning, and policy workflows. It connects core insurance platforms with rules engines, AI models, and external data sources. Insurance underwriting automation in 2026 looks markedly different from earlier pilots.

From breakpoints on your own machine to crashes only real users ever trigger: nine tools, one verdict for each layer of the stack.

Cross-platform builds promise rapid delivery through a shared codebase, yet QA teams encounter stubborn regression roadblocks across Android and iOS. React Native connects JavaScript threads to native host views via an asynchronous message channel. When test runners dispatch actions faster than native components finish rendering, automated assertions trigger false alarms without underlying code failures.

Quick answer: Agentic AI in banking refers to AI systems that don’t just generate text or answer questions – they take a goal, break it into steps, use tools and data sources, make decisions, and complete multi-step workflows (like investigating a fraud case or processing a KYC file) with minimal human intervention, looping in a person only for genuine judgment calls. Walk into any banking technology conference in 2026, and you’ll notice the conversation has quietly shifted.

Software runs mission delivery in the public sector. As agencies modernize and systems grow more connected, the cost of a failed release climbs. The result can be service disruptions, compliance gaps, and loss of public trust. Automated testing is central to managing that risk, but it raises a practical question: which approach is right? Two options come up often: robotic process automation (RPA) and model-based testing (MBT). Both support automation, but they were built for very different purposes.

Handwritten mocks are cheap one at a time. This series built enough of them to show how quickly that stops being true. Nine posts took one package notifier from a function returning "delayed" to a captured response from a real carrier. Along the way, we hand-authored canned successes, failure cases, a spy, a stateful fake, an HTTP server, response fixtures, and contract-drift tests in four languages.

It’s back-to-school season, which means millions of Chromebooks are logging back on. ChromiumOS browser testing, now available on Sauce Labs, helps you make sure your app is ready for them.

August was a month of giving teams more control over their own workspace, more room to trust what they automate, and a quieter but real security and reliability pass underneath it. Test cases get a real version history instead of silent overwrites. Project admins get to clean up their own taxonomy instead of filing a ticket. Playwright finally reaches the private networks the rest of the platform already could, and a critical vulnerability in the Katalon Agent gets patched along the way.

E-commerce platforms face unique performance challenges. Traffic patterns are volatile, with sudden surges during flash sales, promotions, or holidays. Relying on generic load testing – such as steady user simulations or basic server throughput checks – misses the mark. If your tests don’t mirror real shopping scenarios, you risk missing critical issues that can lead to lost revenue and damaged brand reputation during peak events.

Katalon Studio 11.5.0 is available for all users as of August 27, 2026. This release focuses on richer reporting, AI assistance that reaches further into test authoring, a stronger mobile recording experience, a refreshed UI, and the performance problems that showed up in large projects. Here's what shipped, starting with the new features.