Systems | Development | Analytics | API | Testing

As AI-aided software development lifecycles pick up speed, performance teams are left with the familiar challenge of too much work, too few specialists, and results that take too long to analyze. Over the past year, Tricentis NeoLoad has shipped capabilities designed to address each of these problems directly. What started with Augmented Analysis accelerating root cause identification grew into a fully connected Model Context Protocol (MCP) architecture.

Finance leaders are operating in one of the most demanding macro environments in recent memory. Interest rates are moving faster than most models anticipated, reshaping the cost of capital almost overnight. Supply chain fragility has also turned working capital management into a moving target, and geopolitical uncertainty is changing how you plan for the future. Yet for many finance functions, the analytics stack hasn't kept pace with that urgency.

At Ably we've been able to make a transformational change in how we build SDKs as a result of what's now possible with LLMs. In this post, I explain how.

Writing test cases sounds simple until you're actually doing it. You read a requirement, try to figure out what "done" even means for it, write out steps, realize you missed three scenarios, go back, revise, and by the time you feel okay about it, the sprint has moved on without you. This happens to every team working at any real scale. It is just what manual test case creation costs you, and it is the problem autonomous test generation was supposed to solve at a category level.

Your team shouldn’t have a trade-off between mobile app delivery speed and quality. Here’s how to ship with confidence.

AI is at the center of every conversation around operational efficiency, while at the same time being sidelined. In a recent Harvard Business Review Analytic Services survey, only 18% of organizations report that AI is integrated within most of their workflows; twice as many run it as a standalone tool alongside the work. That gap—between AI that assists and AI that operates—is the defining problem of enterprise AI agents.

A dashboard inside an EHR, claims tool, or finance portal is not just reporting. It sits inside a decision path. That changes the bar. With embedded analytics in regulated industries, teams need access control, audit logs, clear metric logic, and a user experience that fits the workflow. Speed matters. So does usability. But compliance-by-design cannot sit after the fact. It has to be built in from the start.

Kong and Persistent Systems partner to make migrating off old API management platforms faster and lower risk Legacy API management platforms were built for a different era. They weren't designed for microservices, multi-cloud deployments, or AI workloads. They're expensive, rigid, and hold engineering teams back. The problem is that migration has always felt hard. APIs are load-bearing infrastructure. Policies are complex. Risk is real. So the old platform stays, and the technical debt compounds.

Ever since AI-driven analytics burst onto the scene, product leaders have been racing to adopt it. Promoted as a way to stay ahead of the curve, AI analytics bring the promise of streamlined processes, personalized recommendations, and a more efficient user experience. But AI advancements aren’t without pitfalls, chief among them inaccuracies caused by AI hallucinations and pilot projects not making it to production.

Astera today announced the launch of Centerprise AI, the agentic evolution of its enterprise data management platform. Centerprise AI embeds proprietary agentic harness across the full data management stack, enabling data teams to design, test, and deploy their data assets, warehouses, pipelines, data models, and analytics in a single platform.