Systems | Development | Analytics | API | Testing

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.

The Head of Enterprise Data, Analytics & AI at IFF sat down with Fivetran to unpack how to successfully implement AI while balancing both innovation and governance concerns.

Pharmaceutical labeling is an ideal use case for AI because it’s a complex process that requires high levels of accuracy. Inaccurate labeling can result in: With recent breakthroughs in AI technology, pharmaceutical companies have rushed to explore its potential. But many have not seen the impact they expected. The problem isn’t the AI. It’s how pharma companies are using AI.

Millions of users rushed to ChatGPT overnight, all craving Studio Ghibli-style art. What started as a fun trend quickly went viral, pushing OpenAI’s servers to their limits. The "Ghibli Trend" wasn't just another online craze — it became a live performance and load-testing scenario for OpenAI. Social media users began sharing Ghibli-inspired AI images, creating a massive buzz.

Despite fears of job loss, QA professionals are leaning into AI faster than ever, according to Katalon’s newly released 2025 State of Software Quality Report. The report reveals that testers using AI tools are twice as likely to fear being replaced by them, a paradox that underscores the profession’s evolving relationship with automation.