Systems | Development | Analytics | API | Testing

Customer data ingestion is the process of collecting customer records from CRM, ERP, product, support, and file-based sources, validating them, and routing them into the systems that power onboarding, reporting, and activation. For B2B platforms, a good approach is a tenant-safe pipeline that can land history, sync ongoing changes, and deliver trusted records quickly.

Virtual users (VUs) are the simulated humans that hit your system during a load test. They’re the load. Where real users come from browsers and apps, VUs come from a test harness. JMeter threads, k6 worker goroutines, Locust greenlets. Each VU sends requests, waits for responses, sometimes pauses (“think time”), and repeats. Aggregate enough VUs and you get traffic that looks like a real audience.

Regulation is pushing for openness, while platforms double down on control — and businesses are caught in between.

The pressure to scale AI is on, forcing most organizations to take a serious look at their legacy technology stacks and reinstate failed or postponed modernization projects. AI both requires and enables a modern enterprise. Traditional barriers to modernization—such as time, cost, and business disruption—are now significantly reduced with the introduction of AI modernization tools.

The healthcare landscape in 2026 is defined by a paradox. While the global healthcare IT market is projected to skyrocket toward a US$ 961.26 billion valuation by 2030 according to MarketsandMarkets, hospital leaders are finding that the shiny new tool syndrome is a recipe for disaster. McKinsey highlights that while agentic AI and ambient listening are transforming administrative workflows, the foundation, the Hospital Management Software (HMS), remains the most frequent point of failure.

Your Mac is cluttered. You can’t find files. Too many apps everywhere. This guide shows you exactly how to clean it up. I’ll walk you through simple steps to organize your Mac workspace so you can actually focus on your work instead of hunting for files.

Testing an AI agent means validating more than final outputs — it means auditing every intermediate tool call, reasoning step, and context decision the agent makes across its full execution trace. Unlike traditional software testing, where passing means the right function returned the right value, agent testing must verify that the correct sequence of decisions produced a reliable outcome for a non-deterministic system.

There's a quiet frustration that lives inside most QA teams, and almost nobody talks about it out loud. You know your product better than anyone. You can walk through a customer journey in your sleep. You spot a broken flow in seconds just by using the app the way a real user would. But the moment someone says "can you just automate that test?" the conversation shifts to a language you never had to learn. Selenium. Locators. Frameworks. Script maintenance. XPath. Java.

Last updated: May 2026 Stateful applications remember information about previous client interactions. Stateless applications treat every request as independent — no memory between calls. The choice between these two designs shapes how an application scales, how it handles failures, and increasingly how AI agents consume it.

For everyone in QA inside the Atlassian ecosystem, the last few months have made one shift obvious. AI is changing how software gets built, and the quality function must change with it.