Systems | Development | Analytics | API | Testing

The rapid adoption of edge computing is fundamentally changing how enterprises process and test data. According to Gartner, 75% of enterprise-generated data will be created and processed outside centralized clouds by 2025 – a dramatic rise from just 10% a few years ago. This surge is fueled by the proliferation of IoT devices and smart endpoints, which generate massive data volumes and strain traditional, centralized cloud models.

In software delivery circles, it’s still common to hear, “Performance testing can wait until just before release.” But that thinking is increasingly out of step with the realities of modern development. Continuous performance testing (CPT) is no longer a nice-to-have for high-maturity organizations – it’s becoming the baseline for any team serious about reliability and user experience. Treating CPT as optional isn’t a conservative move; it’s a risk.

Two engineers used AI coding agents to design, build, and ship a production-ready status page — here's what worked, what didn't, and what we'd do differently.

Engineering leadership faces a persistent dilemma: accelerating release velocity or protecting platform stability. As microservice architectures scale and daily commit volumes grow, test suite execution times stretch from minutes into hours. Flaky scripts and unstable UI locators break build pipelines, forcing senior engineers to spend sprint cycles debugging false positives.

AI is writing more of your codebase every quarter. A new Sauce Labs report surveying 400 U.S. executives and engineering leaders shows most organizations still can't verify it's safe to ship, and the incidents are piling up.

A single source of truth doesn’t just come from centralizing data. You have to engineer context from it, too.

Your data pipeline breaks at 2 AM. Again. By morning, corrupted data has cascaded through dashboards, reports sit empty, and your team spends half the day tracking down root causes instead of building features. This scenario plays out across organizations daily. Data engineers spend 44% of their time firefighting pipeline failures rather than delivering value.

Traditional integration platforms were built for a world of predictable, human-configured workflows. But with enterprise software rapidly incorporating agentic AI capabilities, that world is changing fast. Agentic iPaaS represents a fundamental architectural shift where intelligent agents reason, adapt, and execute integrations autonomously, moving beyond simple "if-then" automation to goal-oriented systems that make real-time decisions.