Operations | Monitoring | ITSM | DevOps | Cloud

I tried thinking back to when the last time I read an actual tutorial that did not include a bunch of em (—) dashes, semicolons, normal dashes, and an unnervingly large quantity of the phrases like “XYZ-thing Alert ” and “Exciting News!”. Well, hold on to your suspenders folks, here we go again. Part 2 is up and it’s a controversial one.

AI agents for SREs were supposed to slash mean time to resolution and eliminate alert fatigue. Instead, most teams got expensive, unreliable tools that burn through tokens without delivering insights. But what if the problem isn't the AI models themselves? Recent benchmarking reveals the real bottleneck: context engineering. When we tested our context engineering approach against conventional methods, the results were dramatic: Scroll down for our benchmark results to see the full comparison.

Your security teams face escalating data volumes, vendor changes, and cost pressures when they migrate between SIEM platforms. Cribl simplifies these migrations by giving you flexible data routing, reducing storage costs, and accelerating time-to-value. How? Let’s look at how a global customer used Cribl Stream to migrate CrowdStrike FDR logs from Splunk to Microsoft Sentinel efficiently and cost-effectively.

Every day, IT teams are flooded with alerts—thousands of messages about performance issues, service outages, or suspicious activity. With so many notifications, it’s easy to get overwhelmed, miss critical problems, or waste time chasing false alarms. Correlating related alerts into groups can help reduce the noise and make sense of everything, but setting up those correlations takes time, experience, and a lot of both system and historic knowledge.

At this year’s.conf25, we introduced an exciting new chapter in observability at Splunk — one that is unified, AI-powered, and agentic — to ensure ITOps and engineering teams are digitally resilient in the AI era.

If you are leading technology innovation in your organization, you know the relentless surge of machine data is rewriting the rules of the digital enterprise. The upside? Incredible opportunities for AI-driven transformation. The challenge? Unprecedented complexity. Today’s leaders are under enormous pressure to unify, analyze, and act on a deluge of data streams across multiple environments.

This blog was authored by Jason Bloomberg, Managing Director, Intellyx BV ‍ Observability depends upon telemetry – the data streaming from various applications, services, and systems that indicate their internal state in real-time. Various tools consume such telemetry to enable both operational and cybersecurity tasks.