Systems | Development | Analytics | API | Testing

Embarking on the test automation journey can be exciting, and daunting, at the same time. It's exciting, because we all know how test automation translates into faster releases, fewer bugs, and most importantly, more bandwidth for QA teams to perform higher-value exploratory tests. It's daunting, because building a test automation tech-stack is full of unknowns: We wrote this article to answer those questions for you and simplify the process of embracing test automation.

The future of software development is fast, automated and constantly changing, so what you should be questioning is: “can my test automation strategy keep up?” Development lifecycles are sometimes cut short and the delivery is needed quicker - without a proper approach, your test automation strategy can become a bottleneck instead of an advantage. With this article, you’ll understand all the features Xray Enterprise brings to the table.

We know the role that AI can play in modern business, and the benefits it brings to employees and customers. But launching and sustaining a successful AI project remains a critical challenge for many organizations. Technology leaders across the globe are being tasked with using AI to drive business success, and it is becoming a vital pillar in reaching strategic goals.

Most software bugs could be traced to validation mistakes. Think about building an app on paper that has no bugs and is flawless, but when they release it to the end user, they keep encountering problems since the software never addresses their problem or fulfills their requirements due to poor data validation. This is where software testing and validation testing enter the picture. It's the activity of making sure the software you've built satisfies end-user expectations, not only technical requirements.

Artificial intelligence (AI) agents are everywhere. Salesforce has Agentforce, Google launched Agentspace, and Snowflake recently announced Cortex Agents. But there’s a problem: They don’t talk to each other. Your customer relationship management (CRM) agent doesn’t know what insights your data warehouse agent has. Your knowledge retrieval agent operates in isolation. Instead of having a connected AI ecosystem, we’re repeating history and creating AI silos.

Next.js (a React framework) and Vercel (the platform created by the Next.js team) are renowned for providing a smooth and efficient development workflow. This allows developers to quickly build, test, and iterate on interfaces for AI features, which is crucial in the rapidly evolving AI space. Features like Fast Refresh, easy setup, and integrated tooling speed things up considerably.

We’ve seen how Snowflake AI tools are transforming outcomes for our customers. From saving 4,000 hours a year on manual email intake to treating more patients in emergency rooms to saving 75% of costs, AI in Snowflake is making a real impact on businesses around the world. That same transformative power is at work within Snowflake, too.

Today, we’re excited to unveil Kong AI Gateway 3.10! This release introduces new functionality to enhance AI governance, reduce LLM hallucinations, and accelerate developer productivity when working with AI. Read on to learn more.

The excitement around large language models (LLMs) and agentic AI is justified. These systems can summarize, generate, reason, and even take actions across APIs — all with minimal human input. However, as enterprises race to integrate LLMs into real-world workflows — especially when those enterprises operate in regulated environments and/or deal in sensitive data — one fundamental question looms large.

AI is quickly transforming the way businesses operate, turning what was once futuristic into everyday reality. However, we're still in the early innings of AI, and there are still several key limitations with AI that organizations should remain aware of to ensure that AI is being leveraged in a safe and productive way.