Operations | Monitoring | ITSM | DevOps | Cloud

Summary Telegraf Controller 1.1 lets teams make fleet-wide configuration changes with a single edit using Global Constants, Configuration Groups, and Configuration Aliases. Configuration Versioning makes every change traceable, comparable, and reversible. High availability, available in Telegraf Enterprise, automatically fails over between Controller instances so agents can continue pulling configurations and reporting health if an instance goes down. Table of Contents.

Earlier this year, we introduced Ivanti Innovators Hub to bring together support, knowledge and community into a more unified experience. That launch established a strong foundation for a more effortless and predictable experience. Now, we're building on it with new enhancements designed to make the Hub more personalized, connected and relevant, with integrated learning opportunities, stronger community engagement and more tailored experiences.

ITSM best practices are standardized ways to design, operate, measure, and improve IT services. For enterprise teams, the most important practices are clear service ownership, consistent incident and problem management, risk-based change controls, trustworthy configuration data, standardized request fulfillment, outcome-based metrics, governed automation, and continual improvement.

This guide shows platform engineers how to evolve beyond basic service catalogs into golden paths that drive real developer adoption. Learn proven patterns for building Internal Developer Platforms that deliver measurable productivity gains through streamlined workflows, self-service capabilities, and developer-friendly abstractions. Your internal developer platform golden paths launched three months ago. Adoption sits at 11 percent. The service catalog has 247 entries, half of them outdated.

Automated incident response means the platform captures the timeline, key events, and decisions as an incident unfolds, instead of a human reconstructing them afterward. Runbooks fire the instant an incident opens: channel created, bridge spun up, Jira and ServiceNow tickets filed, all within seconds. The AI Scribe Agent joins the video bridge on its own and listens to chat, pulling key events out of both the talking and the typing.

Table of Contents Kubernetes resource optimization appears to be a single problem, but the platforms that address it disagree on almost every design decision, starting with how they analyze workload demands. Some set CPU and memory requests from live signals, while others learn a workload’s historical pattern and provision ahead of it.

We are witnessing the fastest expansion of the software development lifecycle in history. Generative AI tools have turned every developer into a hyper-productive builder, and in some cases, turned non-technical team members into creators of production-bound services. But this speed comes with a hidden cost. When the volume of code grows exponentially, the surface area for failure grows with it. The real challenge of modern software engineering is not Day 1 code generation; it is Day 2 operations.

Today, I’m very happy to share a new report, Bridging the AI Acceleration Gap, from Harvard Business Review Analytic Services and sponsored by Nexthink. It examines how employee-led AI adoption is reshaping the role of IT—and what technology leaders need to do next.

Your code review tool does more than display diffs. It shapes how your team shares knowledge, catches bugs, and ships software. With AI-generated code accelerating PR volume, choosing the right code review platform is a decision that directly affects your team’s velocity. GitKraken connects PR workflows, multi-repo management, and AI-assisted review into a single developer experience platform.

Starting an experiment with an LLM has never been easier. Keeping a growing collection of those experiments consistent is another matter. Earlier this year, as more teams began exploring AI features here at Grafana Labs, we repeatedly encountered the same pattern: a new experiment would start, move quickly, and build its own client for whichever model provider it needed. The next experiment would do the same, with a slightly different abstraction for streaming, tools, errors, or provider configuration.