Operations | Monitoring | ITSM | DevOps | Cloud

What do you do when the switch carrying most of your traffic is the one device that cannot tell you what is on it? On high-speed core and data centre links, full flow export pushes device CPU past a comfortable line, so the export gets switched off and the busiest segment quietly becomes the least visible one. sFlow was built for that exact situation.

The StatusGator MCP server now supports a simpler way to connect, making it compatible with more of the AI tools you already use, including ChatGPT and Claude.

Enterprise messaging is not a solved problem sitting still. The last five years have reshaped the technology landscape in ways that are still working their way through enterprise architecture decisions: Kafka's dominance in event streaming, the rise of cloud-native managed messaging (Amazon MQ, Azure Service Bus, Confluent Cloud), the democratization of the Kafka protocol across competing implementations, and now the early emergence of agentic AI as a new category of messaging consumer.

Every vendor in networking has an agent story right now. The useful question for an operations leader is which of those agents can plan, act, and verify against a trustworthy model of the network, and which are assistants that retrieve and suggest, then leave the decision to a person. The direction of travel is settled.

Who in your organization can say, without opening three separate systems, whether last month's critical findings are actually closed? A deployment record answers half of that. The other half needs a rescan, and the rescan often never happens. The vulnerability management lifecycle is that question written down as a repeatable process. It runs from knowing what you own through to proving a fix landed, and it restarts the moment it closes.

A vulnerability scanner will hand you more work in one afternoon than the service desk can clear in a quarter. Thousands of findings arrive ranked by severity, every one of them technically actionable. Fixing them takes weeks, and in most organizations the backlog grows faster than it clears. That gap is what this guide is about. Every tool here scans reliably, scores findings sensibly and reports clearly.

As financial institutions move from AI experimentation to autonomous operations, trusted context, governance, and observability become the foundation for enterprise-scale Agentic AI. Artificial intelligence in financial services is entering a new era. Historically, financial services companies have focused on deploying generative AI to improve productivity, enhance customer experiences, accelerate software development, and streamline operations.

Yesterday (5 August), the WooCommerce team urged store owners to immediately update the WooCommerce Stripe Gateway following the discovery of a critical security vulnerability in the Stripe for WooCommerce plugin. This is the second urgent security update in less than 30 days, with the previous advisory issued on 14 July. It serves as another reminder that cyber threats evolve quickly, and delaying updates can leave your online store exposed. The lesson isn’t limited to WooCommerce.

AI is going to generate a lot of code from here on out, and a lot of bugs along with it. You already know this. We’ve talked about it before. The bigger challenge is making sure you don’t spend all your time fixing the broken code your agents write. You’re going to need a system that makes it easier to fix those issues for you and fortunately, there are a lot of solutions out there for building automated workflows.