Systems | Development | Analytics | API | Testing

Breaking Silos With AI: Aligning QA, Dev, and Product Teams

Software development has never been faster, yet it has never felt more fragmented. QA, development, and product teams often chase the same goals from different directions. Deadlines tighten, requirements shift, and communication gaps lead to rework or misaligned expectations. While DevOps practices have bridged some of those gaps, true collaboration remains a challenge.
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From Loose Threads to Tightly Woven - The AI Shift in Software Design

AI is advancing at breakneck speed-from basic rule-based systems to autonomous agents. Over 240,000 AI papers are published annually, with 1.8M+ projects on GitHub and 80+ large language models released in 2024 alone. Forecast AI spend is expected to top $632B by 2028. Amid the hype, the focus must be on delivering real value and preparing for what's next.

Automate Your Data Workflows: Connect Databox MCP to Make.com

In this video, we show you how to connect Databox to Make using the Model Context Protocol (MCP). Learn how to give your automated workflows and AI tools direct access to your live business metrics, empowering you to easily fetch context, analyze data, and build data-driven automations faster than ever. Links & Resources: About this series: This video is part of our "Chat with Your Data" series, where we explore the Databox MCP.

From Pixels to APIs: The Programmable Economy is the Agentic Economy

The APIs that have been powering websites and apps created a massive market, but there are only up to 8 billion humans consuming them behind pixels. As LLMs are taking over the world — in the form of productized agents first — there will be 100X more machines than humans. The internet built for agents will look very different. Agents don't need to see, scroll, and click graphical interfaces. They can access the internet programmatically.

The Fastest Route to AI-Ready Data - Analyst Studio Demo

Most data teams spend 80% of their time prepping data instead of driving strategy. In the agentic era, that's not just slow—it's a blocker. In this session, Anjali Kumari demonstrates how to go from raw, siloed data to trusted insights in under 10 minutes. This segment shows you how to empower your team with AI-ready data without the pipeline sprawl or surprise cloud bills. In this video, you’ll see a live walkthrough of how to.

Enterprise AI Infrastructure Security Series - 2) Identity Provider Setup, Group Sync & Access Rules

In this video we walk through setting up and testing an identity provider (Azure Entra ID) with ClearML Enterprise, enabling group synchronization to automate user onboarding, and then using platform access rules to secure the resources available to your teams and agents. What we cover: This is Part 2 of our series on enterprise AI infrastructure security.

Confluent Intelligence expands real-time business data to enterprise AI

Support for the Agent2Agent protocol helps connect AI agents anywhere in real time so they can collaborate at enterprise scale. Multivariate Anomaly Detection takes anomaly detection to the next level, stopping problems before they start.

Al boosts developer speed, so why does it slow testers down?

AI slows testers down when it’s added without a tester-first experience. Testers naturally question coverage and intent, so Katalon designs AI around real testing workflows to boost productivity instead of creating friction. — Alex Martins, VP of Strategy at Katalon Follow Katalon for more insights in our series!

Reflect vision-based AI demo | Create one test for multiple platforms

Create a single mobile test that runs reliably on both iOS and Android - without building separate tests per platform or relying on brittle, platform-specific locators. In this high-level demo, we use SmartBear Reflect’s vision-based AI to record a typical workflow in a sample coffee app, where each step is backed by visual context and intent. Then we run the same test across a mix of Apple and Android devices, including an iPhone, to show how Reflect adapts to the environment at runtime and helps reduce flakiness and false positives.