Operations | Monitoring | ITSM | DevOps | Cloud

Microsoft 365 plays a critical role in modern business communication and collaboration with services such as Exchange Online, SharePoint Online, and OneDrive for Business. However, many organizations overestimate Microsoft 365’s native protection and recoverability. In reality, Microsoft 365 operates under a shared responsibility model. While Microsoft ensures infrastructure availability and uptime, organizations are responsible for protecting and recovering their data.

We’ve rolled out an update to the Monitor metrics view to improve both performance and visibility into response time trends for website and ping monitors.

The Observability Gap shows up the moment you try to reproduce a production bug locally. Your traces tell you a request was slow. Your logs tell you which line printed. Neither tells you what was actually on the wire: the headers, the JSON body, the surprise field your client started sending last Tuesday. Until now, closing that gap meant SSHing to a node, attaching a debugger, or shipping a sidecar through change review.

OpenSearch isn't just a search engine anymore. Recent releases moved it into AI infrastructure: agentic memory built in, Better Binary Quantization (BBQ) compressing vectors 32x, token-usage tracking, and a one-command Observability Stack. A stack for building practical AI applications, not just indexing. The catch is that production-sized OpenSearch clusters aren't where you want to prototype.

Is your monitoring stack really giving you clarity, or just more alerts? Your monitoring stack is probably working exactly as designed. That is the problem. As systems grow, most IT and platform teams start to see the same patterns: At this point, traditional monitoring starts to feel limited. This is where teams begin exploring AI in observability. In this guide, we will explain what AI-powered observability actually means, how it works, and when it is useful.

A critical production alert wakes you up: p99 latency just hit 4 seconds. You drag yourself to a terminal, open five dashboards, start correlating log timestamps with trace IDs, dig through 47,000 log lines across eight services, and 90 minutes later, you finally find the culprit: an N+1 database query introduced in a deployment that shipped four minutes before the spike started. An Atatus AI SRE Agent would have identified that root cause and drafted a remediation plan in 28 seconds. Not approximation.

Most form fields ask users for a single value like a name, an email, or a date. But some need a list of values. A plain text input with comma-separated values can technically do the job, but it gives no feedback while typing, no suggestions, and one invalid entry rejects the whole field. The ipl-web TermInput solves this problem. Each value becomes a separate term with its own validation; terms can be enriched, and the input even supports suggestions.

Most conversations about service desk automation stay at the strategy level for too long. Capability checklists and evaluation frameworks matter, but they won’t show you what the platform does when something breaks at 2 AM, or what happens when a single incident crosses four team boundaries before it can close. These scenarios show where simpler platforms start to give way. Teams usually automate the clean, single-system work first.

Security audits that focus only on application code often miss the delivery layer entirely. That is where the most common and most avoidable failures live. Most teams treat security as a layer added on top of a working system. The problem is that the delivery model itself introduces risk before a single line of application code runs. When deployments are manual, environments are inconsistent, or configuration drifts across stages, the system behaves unpredictably.

Enterprise ITOps leaders are realizing that legacy incident management processes are collapsing under the weight of today’s sprawling, hybrid-cloud enterprise environments. Monitoring and observability tools generate a relentless flood of alerts across cloud platforms, infrastructure, applications, and services. The signals are there, the volume of noise makes it harder than ever to identify what’s urgent.