Operations | Monitoring | ITSM | DevOps | Cloud

The Zephyr west tool is a powerful workspace manager, but it is admittedly pretty feature-rich: there’s a lot of functionality buried inside that tool! I thought it would be interesting to share a few tips and tricks that I use with west to make my Zephyr development workflow a little smoother.

The shift from AI copilots to autonomous agents is redefining infrastructure requirements. Discover how to build secure, stateful, and compliant Agentic AI systems using Kubernetes, sandboxing, and observability while meeting EU AI Act standards.

Regular DR testing is no longer a compliance checkbox — it is a critical engineering discipline that determines whether an organisation can survive a real cloud outage with its services and revenue intact. As the AWS Middle East incident demonstrated, regional cloud failures can strike without warning and defeat standard redundancy models, making untested DR plans dangerously unreliable.

On April 1st, I joined Akshay Utture from Augment Code for a webinar on how AI agents use production feedback to improve code.

Autonomous IT is the result of a long operational evolution, from static monitoring and rule-based automation to AIOps and now to systems that can increasingly diagnose, prioritize, and act within defined guardrails. Autonomous IT gets talked about like it appeared out of nowhere. As if someone flipped a switch and suddenly systems started managing themselves. The reality is far less dramatic and far more instructive. What we’re seeing today is the result of decades of incremental progress.

Effective network monitoring requires path validation, not only device polling. Traditional Network Monitoring System (NMS) tools were built for static networks, not today’s hybrid reality. You poll devices, check interface counters, and still struggle to explain why users complain about latency. Traffic moves across SD-WAN architectures, cloud routing layers, and public internet paths that device metrics never capture.

Fragmentation limits AI automation because context is split across systems, forcing humans to bridge the gap. Most IT environments are fragmented by design. Observability data lives in one set of systems, investigation happens in another, and execution sits behind separate tools with their own ownership and controls. During an incident, context does not move with the work.

For years, IT operations teams have been trapped in a frustrating paradox: the data they need to solve critical issues is right at their fingertips, yet entirely out of reach. Accessing it requires engineers to master complex, platform-specific query languages, dig through endless layers of dashboards, and hunt for the exact visualization that holds the answer. Under the intense pressures of modern speed, scale, and complexity, this rigid model is breaking down.