Systems | Development | Analytics | API | Testing

There is a specific kind of confidence that comes with deploying software. Not just “the tests passed” confidence, but the kind that comes from knowing the services your application depends on still behave the way you expect them to. Preserving that integrity becomes harder as systems grow, teams move faster, and AI agents become active participants in delivery workflows.

The most dangerous multilingual AI failure is a fluent output that feels native enough to trust, but is wrong enough to mislead.

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.

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.