Operations | Monitoring | ITSM | DevOps | Cloud

What does it take to make AI and automation actually work in enterprise IT? In this episode of Agents of IT, Resolve’s Zach Austin sits down with Nick Dimmock, Co-Founder and CEO of TechWorks, to discuss how IT automation is evolving, why trusted data matters, and what organizations need to build before AI can deliver meaningful business outcomes.

Telemetry Talks episode 8 is here We sat down with OTel maintainers to talk about the future of the community, GenAI semantic conventions, contributing beyond code, OTel in Practice, and what they’re currently building, writing, organizing, and experimenting with across the CloudNative and OpenSource ecosystem. A conversation about where OTel is today and what comes next. Playlist Resources for Further Learning.

In the September edition of the Kubernetes Monitoring Helm chart office hours, we discuss the version 4.4 and 4.5 releases, the upcoming 4.6 feature release, and the next 5.0 major release.

Azure architecture diagrams go out of date fast, and management-plane data alone misses the runtime connections that matter most. In v5.4, diagrams move to their own Diagrams tab in Azure Documenter. Alongside the enhanced network and workload diagrams, there is a new Resource Visualizer diagram. Scope it by subscription and resource group, or write your own custom Azure Resource Graph query to define exactly which resources to include.

"Our Azure bill went up 20% last month." Without context, finance only sees a rising cost. Unit economics gives them the full picture. Turbo360 lets you overlay business KPIs on your Azure spend. Track units like orders, active users, document views, or monthly recurring revenue alongside cost, and see your cost per unit month over month. Now the conversation becomes: "Orders went up 150% and our cost per order came down." That is a story about efficiency, not overspend.

You wouldn’t ship untested SQL Server database changes – Why is Databricks different? Databricks is where the data estate is growing, and increasingly where AI workloads run and generate change. But schema change there still happens the hard way: views and stored procedures managed through manually versioned scripts, drift between workspaces discovered when a deployment fails, and no reliable record of what changed, when, where, or why. As AI raises the volume and speed of schema change, these gaps widen.

Most teams collect the profiles and traffic data that explain a slowdown. Almost nobody has time to read it before users notice. In this Product Highlights conversation, Sylvain Guittard, Senior Director of Product at Upsun who leads the team behind the Upsun console and CLI, breaks down the Upsun Cloud Performance Agent, the first background agent running on Upsun Cloud. His take: "We monitor everything, we feed that into an agent, and the agent will be capable of finding what the bottlenecks are in your application. And on top of it, it gives you a patch, or a way to fix it." We get into.

Anyone can claim an AI agent will fix your performance problems. This demo shows exactly what it looks at and what it hands back. In this Product Highlights conversation, Sylvain Guittard, Senior Director of Product at Upsun who leads the team behind the Upsun console and CLI, runs the Upsun Cloud Performance Agent live on a demo project. His take: "You have a patch that is already available, and a recommendation, so you can see if it fits or not to your context." We get into.

Every workday runs on digital experience, so what happens when logins are slow, devices lag and IT is buried in alerts? See how ignio AI.Digital Workspace from Digitate helps IT teams predict, prevent, remediate and assure a great digital employee experience.

Jira Planner is an AI planning agent in Jira that turns high-level product requirements into ready-to-build plans (requirements, technical specs, and Jira work items) grounded in your actual codebase, architecture, and Confluence context. In this video, you'll see how Jira Planner brings due diligence to the pre-build phase, so engineers and AI coding agents start from a plan that reflects the real code instead of a vague prompt. It's useful for product managers, engineering leads, and developers who want to cut the rework that comes from AI guessing at requirements.