Systems | Development | Analytics | API | Testing

Ever found yourself saying, "But it works on my machine!" when a bug pops up in a microservices environment? It's a common and frustrating problem. Unlike a monolithic application, microservices are a collection of independently deployed services that communicate with each other. This complexity makes it difficult to reproduce real-world issues on your local machine, as you may not have all the necessary services and dependencies running. But what if you could take a snapshot of a running application's behavior and bring it home for debugging?

Open source brings real power, but its goals may not match your organization. Look at community health, whether it fits your use case, and how you will handle support, updates, and patches. Consider the integrations your stack requires. Weigh these against a commercial platform that offers formal support, a roadmap, and hosted infrastructure to reduce your maintenance burden. — Coty Rosenblath, CTO at Katalon.

In today’s AI era, everyone wants quick answers and instant outcomes. But without trusted data, those AI “wishes” can quickly backfire. That’s why Qlik created the patented Qlik AI Trust Score, now available in Qlik Talend Cloud - Qlik’s unified, enterprise-grade platform for data integration and data quality in the cloud.

All AI problems are data problems—and one of the biggest is getting AI agents to talk to each other. This special episode with Sean Falconer dives into how agents built by different teams often end up stranded in “intelligence silos,” unable to collaborate or share context. The result? Fragmented AI that struggles to deliver real business value.

AI agents are only as powerful as the data they can access and share. Confluent’s Sean Falconer explains how when agents can’t communicate effectively, intelligence silos form, limiting their potential and slowing innovation.