Systems | Development | Analytics | API | Testing

AI can now handle the slowest parts of test management, inside Jira. – The Zephyr Agent for Rovo creates test cases and links them to your work items, in the projects where your team already plans and builds. This guide covers how testing is changing in the AI age, what the agent is, where to find it, and how to run your first task.

APIs are the backbone of modern software. They connect microservices, power mobile experiences, and make integrations possible across industries. For all their importance, API testing remains one of the most fragmented, manual, and maintenance-heavy parts of the software development lifecycle (SDLC). So as development accelerates in an AI-disrupted SDLC, application integrity – continuous, measurable assurance that your software just works as intended – becomes harder to maintain, not easier.

Claude Code shipped workflows: reusable, versioned instructions an agent can follow instead of improvising a process from scratch each time. We used that feature to hand Claude Code our entire QA job. Not "write a test case." The whole loop: read the requirement, design the coverage, run it on the platform, file the defect, report back with evidence. The kind of work a QE does in a day, compressed into one skill triggered with a sentence.

Ask a Marketing Lead how the quarterly plan actually gets built. Not the strategy, the mechanics. The answer, in most teams, is a spreadsheet: spend pulled from five ad platforms with five different backends, pipeline exported from the CRM, last quarter’s numbers copied from a deck, targets negotiated in a separate thread. One customer described their version of it to us in a sentence that needs no editing.

Shopify’s MCP servers give Claude real command of your store, from storefront conversations to bulk product updates. Analytics is the one job they were never built for, and pairing them with Databox MCP closes that gap.

Implementing a PXM solution provides numerous benefits to your organization, from improving efficiency to increasing sales, reducing returns, and promoting customer loyalty. Today, we’re going to explore these benefits in more detail. Interested in the distinctions between PIM and PXM? See our breakdown of how they differ (but are also similar) here.

Ask an AI for K-pop concert advice without saying the group, city, date, or budget. It may confidently send you to a BLACKPINK tribute night in Cleveland with a $400 resale ticket. The AI was plenty confident. It just had nothing real to go on. That is exactly what happens when developers test AI applications with invented traffic. The test may look reasonable. The result may even pass.

The AI-analytics category has one operating assumption: AI’s job is to do more of what humans currently do. Adoption is measured by delegation surface. Success is when AI takes the whole task. Ask the executives running AI daily what they refuse to delegate, and you get a different story. Actually, you get the same story, from operators who do not know each other, running different companies, across different functions.