Systems | Development | Analytics | API | Testing

Watch an AI Agent Connect to External Tools and Systems in Minutes Using MCPs | Live Demo

Building AI agents is just the beginning. The real value comes when these agents are connected to your enterprise data and systems in a meaningful way. This means moving beyond isolated tasks and enabling agents to interact with real-time data, external applications, and business logic through seamless integration. But traditionally, that requires coding, API management, and technical expertise. What if you could skip all that?

Why APIs and Tools are Critical to Your AI Strategy

As enterprises race to integrate AI into their workflows, a critical truth is emerging: success isn’t defined by the size of your model—but by the strength of your infrastructure. Join Hugo Guerrero, Principal Technical Product Marketing Manager at Kong, and Alex Salazar, Co-Founder/CEO of Arcade.dev, for a live conversation on how APIs and the right tooling can unlock the full potential of AI agents in real-world enterprise environments.

Driving Innovation with NVIDIA AI Data Platform: A View from Hitachi Vantara

The rapid acceleration of AI adoption is transforming how enterprises design their data infrastructure, driving the need for robust, scalable, and energy-efficient solutions. At Hitachi Vantara, we’re building the future of AI storage by collaborating with NVIDIA to close the gap between data and AI compute. Our mission: help organizations unlock faster, smarter insights with an AI-ready data pipeline.

What Is Code Refactoring?

Have you ever looked at your code and asked yourself, "Who wrote this mess??" And suddenly you realized it is none other than you. I’ve faced this situation a lot—your own code seems like a mess if you review it after 2 or 3 months. Do you know the reason why? Yes, it’s because there is no refactoring in the code In this blog, we’ll explore what code refactoring is, why it’s important, and walk through a few examples.

The Easiest Way to Power Real-Time AI: Confluent Announces Delta Lake Support & Unity Catalog Integration for Tableflow

In the age of AI, the hunger for fresh, reliable data to power machine learning (ML) models and real-time analytics is insatiable. Yet, organizations frequently hit roadblocks when trying to bridge their operational data in motion, typically flowing through Apache Kafka, with their data at rest in data lakehouses. On one side, you have the data streaming platform, the central nervous system managing the real-time flow of business events.

What Companies Get Wrong About Data Ownership and What to Do Instead

Most companies believe they own their customer data. Most are wrong. Data is your most powerful asset for fueling decisions, improving customer experiences, and providing a competitive edge. But if your customer, marketing, or product teams rely on third-party analytics tools, there’s a great chance you don’t actually own your data. It’s processed, stored, and sometimes even monetized by vendors who decide your access and control levels.

Navigating Nested Maps in Katalon Studio: Finding Values Without Key Specification

The information in this blog post is based on a real-life scenario shared by a user on our Katalon Community forum and is intended to inspire peer-to-peer discussion and collaboration. Please always test solutions thoroughly before implementing them in a production environment. Feel free to continue the discussion here.