Systems | Development | Analytics | API | Testing

AI-generated API tests in Katalon Studio 11.4 #Katalon #APITesting #TestAutomation #OpenAPI #QA

Katalon Studio 11.4 can now generate API test cases with AI. Import your OpenAPI specification and Studio automatically creates the Web Service requests in an API Collection. From there, generate tests and save them straight into your project. The AI doesn't just show you a preview. You get real test cases covering positive flows, boundary values, NULL values and empty strings, ready to run and build on like any other test case in your project.

Jedify CEO On Building Enterprise AI That Understands Your Business

Assaf Henkin, Co-Founder and CEO of Jedify, joins the Snowflake Summit 2026 News Desk to discuss how enterprises can build AI applications that truly understand their business context. Drawing from 15 years of experience building open source intelligence platforms, Henkin shares insights on balancing innovation with operations, staying true to founding principles while adapting to market changes, and how Snowflake's AI Data Cloud is enabling Jedify to reach new heights in the agentic AI era. Learn what's driving enterprise AI adoption and what founders should focus on for the back half of 2026.

How Appian Provides AI Guardrails and Controls

AI agents make thousands of decisions per day, at volumes no human-centered governance model can realistically supervise. According to IBM's 2026 Tech Leader Study of 2,000 CIOs/CTOs in 33 geographies across 19 industries: The study concludes that organizations face a trap: prioritize speed, and governance falls behind; or prioritize safety, and deployment stalls, weakening the organization’s competitive position.

Introducing AgentSpot: Your Workforce, Multiplied

Your team already knows when a campaign starts to underperform, when spend spikes, or when web traffic shifts. What you don't have is the speed to turn that insight into action. Someone still has to investigate, decide what to do, pull in the right people, and coordinate the work. That takes time. As a data-driven CMO, I've lived this every day. I can know the instant something changes in the data, but there's still a large gap between insight and action. Today, we're closing that gap.

Building in the Fast Lane: How AI and Internal Innovation Birthed AgentSpot

The journey to AgentSpot didn't start with a traditional product roadmap or a speculative “what if” from our R&D labs. Instead, it was born out of a growing friction within our own walls and became a "frontier R&D project" fueled by engineers exploring the internal potential of generative AI. When we first launched SpotGPT, our internal genAI application (similar to ChatGPT, but trained on internal resources) we saw immediate and massive adoption.

Making Better Release Decisions with AI Test Prioritization

You're preparing for a release readiness meeting. The Test Plan contains hundreds of Tests. Development continued until late yesterday, several Defects were resolved overnight, and only a few hours remain before stakeholders need an update. There is enough time to execute part of the Test suite, but not all of it. The question isn't whether testing should continue. It's which Tests should be executed first. Every release forces QA teams to make prioritization decisions.