Systems | Development | Analytics | API | Testing

The rise of AI-assisted coding has transformed how software is built. With tools generating entire features in seconds, the bottleneck is no longer writing code—it’s verifying it. Because AI can generate boilerplate and handle API integrations instantly, more service changes are being pushed into authentication logic, API calls, and configurations. Teams desperately need a way to verify these changes before merging, especially when the code touches external dependencies.

Agentic Testing and QA is a practice in which AI agents operate directly on a project — reading files, planning tasks, generating framework code, and interacting with a browser — rather than simply answering prompts inside a chat window. Tools like Claude Code bring this capability to the terminal, giving QA teams a command-line assistant that understands repository context, proposes changes before applying them, and generates test assets across Playwright, Selenium, and API testing workflows.

AI agents do not just respond. They interpret goals, use tools, follow workflows, apply context, and sometimes change real systems. That means they need a different kind of testing.

Enterprises are buying AI infrastructure faster than their platform teams can operationalize it. Dell and ClearML are working together to close that gap, giving enterprises a faster, simpler path from Dell AI Factory hardware to a production-grade AI platform. Dell carries the hardware. ClearML provides the AI infrastructure layer on top. Together, the two give platform teams a way to deliver AI as a service to their organization without a multi-year integration project.

Modern performance testing is evolving beyond the traditional choice between enterprise platforms and open-source tools. Teams increasingly need the flexibility of JMeter, k6, Gatling, or Locust combined with enterprise-grade reporting, scalability, security, and support. A new generation of platforms helps reduce operational complexity, lower total cost of ownership, and accelerate adoption through AI-assisted workflows and simplified onboarding.

There's a pattern we see repeatedly in enterprise AI projects. A team identifies a compelling use case. They build the model. They staff the project. Then they spend the next six to eighteen months trying to solve a problem that was never on the roadmap: their data isn't ready. Not because it doesn't exist. It exists everywhere: in cloud warehouses, on-premises databases, SaaS platforms, and data lakes across multiple regions.

New Sauce Labs Real Device Cloud Agent — available now in the watsonx Orchestrate Agent Catalog — can enable enterprise teams to trigger real-device tests, manage fleets, and validate app quality using natural-language commands.

The Apple Push Notification Service (APNs) allows developers to send real-time alerts and data to Apple devices. But it can create a number of problems as your app scales including silent throttling, deep link errors and push payload incompatibility. This post will help you proactively avoid these issues. You’ll learn about: This guide is intended for developers already using push notifications or planning to operate notification systems at scale.