Operations | Monitoring | ITSM | DevOps | Cloud

Many outages, slowdowns and failed transactions start in the database, often well before anyone outside the data team knows something’s wrong. By the time they do, customers are already affected: payments fail, services stall and people can’t do what they came to do. The organisations that keep this risk under control close three gaps. They stop problems before they disrupt customers and the services they depend on. They find the cause in minutes, and fix it quickly.

Automating releases, semantic version bumps, and API calls often involves configuring branch permissions and storing long-lived credentials in pipeline variables. Managing credential rotation adds an ongoing maintenance burden.

Does your IT team know which applications, users, or devices are consuming the most network bandwidth? A video call freezes, file transfers slow down, or a network link becomes congested. Without traffic analysis, finding the source of the problem can take hours. Network analyzer software helps your IT team: However, tools differ in how they collect traffic data. Some analyze flow records, while others also inspect packets to provide more detail.

Why does an application that runs well in the data center slow down once one of its tiers moves to AWS or Azure? Often the servers are healthy and the cloud console shows no faults; the delay lives in the connection between them, where neither view reaches. That blind spot shows up in familiar ways: So these are design problems before they are operational ones.

Improving an AI agent often involves changing its prompts, models, retrieval methods, or tools. But determining whether those changes actually improve performance can be difficult. A revised prompt might produce better answers for a handful of test cases but perform poorly on unexpected user requests. A cheaper model might reduce token costs while introducing tool-calling errors or longer execution times.

Table of Contents In Part 1: Agentic AI at Scale – Building the Kubex Agentic AI Platform of this series, we laid the foundation for scaling agentic AI across cloud infrastructure. We examined why traditional ops workflows get stuck in reactive loops of dashboard watching, why static context limits intelligence, and how the Kubex platform uses context engineering and a distributed nervous system to bridge the gap between telemetry and deterministic actions.

A vendor saying it plays in a market is one thing. A respected independent research firm saying so is another. That’s why I’m pleased to share that Kubex has been included in Forrester’s The Cloud Cost Management And Optimization Solutions Landscape, Q3 2026.

Picture the bridge call at 2 am. A Sev1 has taken down a critical service, and the paging storm has pulled forty people out of bed, even though only six of them will ever touch the fix. The first hour disappears into roll call and trying to figure out which changes shipped last night, which alerts are related, and who actually owns the failing service.

Choosing a software engineering intelligence platform for an enterprise team means weighing SOC 2 compliance, data privacy controls, and the ability to scale across hundreds or thousands of contributors. You need visibility into DORA metrics, AI coding tool impact, and delivery velocity without compromising how your repositories and developer data are protected.

Sometimes you just want to fetch all Icinga 2 objects of a given type from the API and do something with them. Then you notice that Icinga 2’s memory usage jumps up with every large request and never comes back down, as if each HTTP request leaked a huge chunk of memory. And once enough of these requests run in parallel, no matter how much RAM the machine has, Icinga 2 runs out of memory and crashes. Here’s what was really going on, and how we fixed it in Icinga 2 v2.16.