Operations | Monitoring | ITSM | DevOps | Cloud

AI is set to transform our technology landscape. For many of us working in software, it already has — developers are now writing more code, building more features, and deploying more applications, faster. For the teams supporting IT and software services that means more applications to support, across a greater breadth of technologies, and with more complexity (that is probably less well understood by the developers who created it). Your operational tooling needs to keep pace.

When designing effective error handling for React apps, the troubleshooting information you collect and display is critical. React errors can stem from a variety of causes, including user misconfiguration, backend and network issues, and mismatches in browser environments. Instrumenting your code to log critical context, including feature names, user data, and session activity, enables you to quickly identify where these errors originate.

We run Postgres under an operator for automatic failover. That is a promise about what happens during a failure, so the only way to know you have it is to cause the failure and watch. The docs tell you what should happen. A config review tells you which knobs are set. Neither tells you how long an isolated primary keeps accepting writes after its replacement has been promoted, and that number decides whether a failover is clean or leaves you with two versions of your data.

Enterprise AI agents clear every demo and pilot, then hit a compliance wall. The gap isn't technology—it's architecture. Discover why governance must sit inside the execution flow across six control points, not bolt on afterward as an afterthought at input and output only.