Systems | Development | Analytics | API | Testing

Low-code automation testing is changing the way teams build and maintain tests. With less scripting, intuitive visual tools, and reusable components, testers can work faster and collaborate better, no matter their coding background. It’s no longer just for QA engineers. With modular low-code components, visual test logic, and hybrid test creation, developers, testers, and business analysts can all contribute to quality. The process becomes faster, more inclusive, and easier to scale.

Ever noticed how you’re always getting relevant ads, whether you’re streaming on Netflix or shopping on Amazon? Or how sometimes, just thinking about something seems to make it appear on your phone? It feels like every application somehow knows what you’re thinking, serving up personalized suggestions with high precision.

Today, speed is everything, and that has put businesses under immense pressure to develop and deploy applications faster than ever before. The rapid expansion of low-code/no-code (LCNC) development platforms has been driven by this requirement for speed.In fact, Gartner predicts that by the end of 2025, a staggering 70% of new applications developed by enterprises will use LCNC technologies.

System integration is a fundamental requirement for modern business operations. This brief technical overview clarifies Linx’s integration capabilities and provides a reference for developers and architects considering their integration options.

Automation has long been a key driver of efficiency. Traditional RPA and IDP technology promised to relieve carriers from rekeying, extracting data from forms, and other repetitive tasks. At Appian, we saw early that automation in isolation doesn't achieve transformative outcomes. Why? Because AI has too often been deployed at the edges of workflows: copilots, chatbots, or analytics dashboards that assist us when prompted.

Historically, software development clearly separated business and IT roles. Business teams defined the business requirements, while IT teams built digital experiences (e.g., customer-facing applications) based on these requirements. There was a continuous feedback loop where business teams reviewed and provided feedback, and IT teams made necessary changes until the digital experience was public-ready.

Enterprise automation didn’t start with AI agents, but they’ve had a much bigger impact than earlier automation methods, such as software scripts or bots. Modern AI agents can do a lot more than tackle repetitive tasks. They can reason through complicated workflows, choose the best course of action, and access tools to execute said action. But to do all this, AI agents require interoperability. They need to be able to connect to numerous tools, databases, services, and APIs.

As businesses race to become data-driven, the ability to quickly build and iterate on data workflows is more critical than ever. Traditional ETL and ELT processes, while powerful, often require extensive coding, long development cycles, and high maintenance overhead. Enter low-code data pipelines: a modern, visual-first paradigm enabling faster development, broader accessibility, and better scalability.

As enterprise data increases in volume, variety, and velocity, the need for a new data architecture is becoming clearer. As AI moves from generative to agentic, can enterprises also envision and adopt an agentic data architecture? It’s true that we’re already seeing AI agents implemented in functions such as customer support and marketing. But what if we could do the same for data management?

In insurance, the claims process is the real “moment of truth.” It's when customers find out if their insurer is truly there for them. They don’t just want fair treatment—they also expect their claims to be handled quickly and easily. But the reality? Claims often take way too long because of outdated, clunky processes. And the growing tsunami of data needed to adjust a claim can create information overload for an adjuster.