Operations | Monitoring | ITSM | DevOps | Cloud

Our previous blog gave a quick overview of an all-too real scenario involving poor Microsoft Teams performance and frustrated VIP users. The situation, picking up on our recent Power Moves webinar, centered on a big board meeting held over Teams that suffered from multiple call quality issues — spurring the CEO to pay a stormy visit to IT. In that case, the issue had already happened, and our point was that with native Microsoft tools, it can be hard to get to a precise root cause quickly.

Let’s not mince words; there’s a special place in hell for the password reset ticket. It’s the most boring, most avoidable, and arguably the most expensive waste of time on your service desk. And yet, in 2025, most enterprises still treat password resets like it’s 2005. They route them through manual queues, bury IT teams, and frustrate users who just want to log back in. Even when the password reset is finally resolved, nobody comes away from the experience feeling like a winner.

At Cribl, we’ve talked a lot about epochs. A moment in time when there was a before and after. AI, and specifically agentic AI, is an epoch. The way we work is going to forever change. There have been many such events in our lifetimes: the PC, the Internet, and the smartphone. AI will change how we work forever. Prior to the PC, there were people whose jobs were literally titled “computer”.

Investigations move fast. Data is messy. And today’s analysts are expected to connect the dots across massive datasets and various tools—while documenting every step and sharing results with stakeholders. What does that look like? A security investigation may involve 10 or more queries—each one filtering, transforming, and analyzing data from a different angle—duplicated across multiple browser tabs so nothing gets lost.

Stay in the loop on key incidents with Auto-Watch and ensure data consistency at every milestone with Required Fields by Incident Type.

Enterprises are prioritizing digital transformation and agility, yet most lack the structural readiness for what's next. When 95% of financial services professionals believe there's little to no risk in delaying system modernization, even as the UK's FCA issued over £319 million in fines for non-compliance in just six months, it's clear many are mistaking surface upgrades for true adaptability.

I'll start this, the third and final entry in my series on APM and Observability, which was originally inspired by my contribution to an APMdigest article, by once again pointing out that APM tools can be built with observability in mind. Many are, in fact. And the ones that aren’t don’t turn into a different type of tool. In my experience, it's more that there's a difference of mindset.

From Microsoft Copilot to ChatGPT, AI applications are quickly becoming everyday workplace tools. But for many organizations, turning on these capabilities isn’t as simple as flipping a switch. Enterprise licenses for AI tools can cost millions, yet few companies can confidently say employees are using them effectively, or safely. The reality is that most AI rollouts start strong but stall fast.

If you’re building high-performance systems, Golang has probably earned a spot in your stack. Its speed, lightweight concurrency, and quick compile times make it ideal for scalable APIs, microservices, and distributed systems. But those same qualities that make Go powerful can make performance monitoring tricky. Goroutines run fast and in parallel, which means a simple CPU or memory graph doesn’t always tell you what’s slowing things down.

From pipelines to warehouses, modern operations generate more distributed data than ever, with equipment and connected devices spread across factories, grids, and remote sites. A single, centralized historian can no longer handle this volume or distribution. Without change, organizations risk fragmented visibility, higher costs, and slower responses.