Operations | Monitoring | ITSM | DevOps | Cloud

June was about finishing touches. The fun part. Node.js support, which we previewed in May, is live. Anomaly detection graduated with a rebuilt algorithm, per-monitor controls, and access from the API, CLI, and MCP server. We also kept pulling on the same thread from recent months: Scout data should be reachable from wherever you actually work. The MCP server now covers historical insights, anomaly events, and 30-day metrics. Discord is a notification channel. The CLI has scout anomalies.

An IT asset audit is one of those processes most IT teams know they should run regularly and rarely do well. Records drift from reality, licenses go unchecked, and the first sign something is wrong usually arrives as an auditor's request or an unexpected vendor notice. This guide walks through how to perform an IT asset audit step by step, and how to structure the process so it never requires a last-minute scramble.

Observability platforms collect pre-instrumented logs, metrics, and distributed traces to monitor production systems and surface failures to human engineers. The adoption of AI into engineering has led observability providers to offer those same signals to agents. This is often packaged as AI observability, but the signals themselves were designed around a human investigation loop. AI coding agents work faster, consume data differently, and need feedback as they work rather than after deployment.

Since 2020, the modern workspace has fundamentally changed, where employees now operate across a mix of office, hybrid and remote locations. Critical systems are now distributed between data centres and public cloud platforms, and most corporate data lives in the cloud. This shift has expanded the attack surface for many businesses.

Over 90% of mid-sized and large companies estimate that a single hour of downtime now costs more than $300,000. The clock starts the moment something breaks, whether anyone has noticed it or not. And most outages don't start with alarms. They begin with a small issue inside the network: an overloaded switch, a saturated link, or an unstable interface. Left unnoticed, those small issues grow into user complaints, stalled work, lost revenue, and damaged customer trust.

For private medical practices in 2026, secure and efficient communication is non-negotiable. Standard consumer messaging apps like iMessage and WhatsApp are not compliant with privacy regulations and create significant risks for both patients and providers. Adopting a dedicated, HIPAA-secure messaging solution is essential for protecting patient data and streamlining clinical workflows.

AI agents are nondeterministic, multi-step, and opaque. When one fails in production, "the model said something weird" is the cheapest, most useless line in your incident postmortem. To debug agents the way they actually run, you need telemetry that captures all of it, in order, with enough context to reconstruct what happened. The OpenTelemetry GenAI Semantic Conventions give you a vendor-neutral way to do exactly that.

New research reveals why Australian businesses are entering the new financial year with bigger AI budgets and the same unsolved problem. One in three Australian businesses exceeded their AI budget last year. Yet, half of them plan to increase AI spending again this year. Yet the behaviour that caused those budget overruns remains largely unaddressed.

Why do 40% of alerts received by security teams today go completely uninvestigated? It’s not due to a lack of concern but instead caused by shortening attack windows and compounded by overwhelming tech sprawl. Today’s security teams are operating in a threat landscape defined by escalating attacks, tighter budgets and mounting alert fatigue. Organizations process an average of 960 security alerts per day, and large enterprises handle more than 3,000 daily alerts across roughly 30 tools.