Operations | Monitoring | ITSM | DevOps | Cloud

Autonomous IT is the operating model where systems detect, decide, and act so your engineers spend less time fighting fires and more time defining what ‘good’ looks like. On a typical day, a mid-size enterprise generates tens of thousands of alerts across on-prem infrastructure, multiple clouds, and AI workloads, including every endpoint. Most of them don’t need a human. A few of them do, and telling the difference, fast enough to matter, is where IT teams are losing ground.

Operators today are navigating unprecedented complexity—rising costs, accelerating customer expectations, and increasingly dynamic networks. In this recent video interview, my colleague Kevin Wade and I explore why AI‑driven automation has shifted from a “nice‑to‑have” technology to a core business requirement for telecom operators and beyond.

LLM applications rarely crash. They degrade quietly. Once these applications are shipped to production, subtle quality failures become harder to catch with traditional signals. Tone shifts, hallucinated details, off-topic responses, and incomplete reasoning can emerge while latency and token usage look stable.

Kubernetes environments generate a constant stream of signals across clusters, nodes, pods, and workloads. For teams that have standardized on OpenTelemetry (OTel), maintaining ownership of that data is critical. But in practice, many observability platforms require translation into vendor-specific data formats, leading to fragmented product experiences, blank dashboards, and uncertainty about data integrity.

Many organizations rely on Oracle Fusion Cloud Applications to run core business workflows across finance, HR, and supply chain operations. Because these SaaS-based applications run on Oracle Cloud Infrastructure (OCI), engineering teams have limited visibility into their performance. Without direct access to the underlying stack, they often lack the signals needed to detect regressions or investigate degraded user experience.

For many EUC and Digital Workplace leaders, the challenge with digital employee experience (DEX) isn’t the technology, it’s building alignment. You can see the data. You know where friction exists. You can quantify disruption, productivity loss, and inefficiencies. But you struggle to achieve your targets, because you need buy in from other teams, and right now, they don’t want to hear anything about DEX. Security has different priorities. Application owners are focused on releases.

Recent enhancements to eG Enterprise have added functionality to allow you to proactively test your internet speed with synthetic monitoring (“robot” tests that simulate real user activity). Using the new functionality you can proactively monitor internet speeds 24×7 from any location. The performance and quality of an Internet connection plays a major role in any IT environment. Use cases for this new functionality include.

Oh Dear's dashboard now supports dark mode. You can choose between light, dark, or system-based theming, and your preference is saved to your profile so it follows you everywhere.

Most engineering leaders today understand that diversity matters. They've built teams that reflect a range of backgrounds, functions, and experience levels. They run postmortems, retrospectives, and architecture reviews that bring multiple voices to the table. They believe, not unreasonably, that this variety of perspectives leads to better decisions. But there's a problem hiding inside that assumption that can undermine everything: who people are is a surprisingly poor predictor of how they think.

On March 18, Jensen Huang took the stage at NVIDIA’s GTC conference in San Jose for a keynote that ran well over two hours — covering everything from CUDA’s 20-year history to humanoid robots that may one day wander Disneyland. But buried inside the spectacle was a remarkably clear-eyed articulation of the economic forces now bearing down on every enterprise that builds on cloud infrastructure.