Systems | Development | Analytics | API | Testing

AI test automation frameworks are transforming how teams build, execute, and maintain test suites by embedding intelligence directly into the testing workflow. Start small with a pilot framework implementation, prove ROI on a single project, then scale AI testing capabilities across your organization. Building an AI test automation framework requires more than bolting AI features onto existing test suites.

Agentic Test Automation for Tosca revolutionizes software testing. Using only natural language prompts, it automatically generates complete, executable test cases — allowing QA teams to keep pace with modern AI-driven development. This latest update expands support for new enterprise technologies and uses Tosca’s automation engine to become even more powerful. Enterprise customers can now create complex, end-to-end tests that are built and supported by Tosca’s proven technology.

Determine the exact data lakehouse capabilities you’ll need for your use case, and which vendors offer them.

Every team in your company has the same problem: they need answers from data, but getting them is never fast. Marketing wants to know which campaigns are working. Sales wants to know which deals are stalling. Leadership wants to know if the business is on track. Each team asks different questions, but they all end up in the same place—waiting for someone else to pull the numbers. What if your teams could just ask questions and get answers instantly? That’s what Databox MCP enables.

Struggling with flaky tests and slow automation? Explore the best AI-powered testing tools of 2026 — and why Sauce Labs stands out.

Discover how AI helps continuously test games 24/7, catch bugs earlier, and eliminate dev crunch.

This week, we had a chat with one of our enterprise customers about their thoughts on AI inside their Yellowfin environment. They’re currently running version 9.15 and use Yellowfin as the primary BI layer inside their app.

When building AI experiences, choosing between WebSockets and HTTP isn't always straightforward. Which protocol is better for streaming LLM responses? How do you maintain continuity when users switch devices mid-conversation? Should you use both? The answer depends on the type of AI experience you're building. Modern AI applications often require both protocols, each serving different purposes. The key question is: how do you decide which communication pattern fits each scenario in your AI stack?