Operations | Monitoring | ITSM | DevOps | Cloud

Ivanti Neurons for Secure Access: Full Platform Demo | Ivanti Discover how Ivanti Neurons for Secure Access gives your security team complete, real-time visibility across your network — with automated threat detection, Zero Trust access control, and centralized gateway management, all from one cloud-based platform. Watch the demo and see what unified secure access looks like in action.

Meet your digital teammate. Persona-based AI agent designed for critical ITSM workflows. Transform IT operations with AI agents that plan, coordinate, and execute autonomously, delivering measurable business impact through intelligent automation. Your conversational front door to IT that replaces forms with natural language, cutting ticket load and improving data quality through guided capture.

People want to support your community. Sometimes you just have to invite them in.

If a problem reached you, it means nobody else could solve it. That makes it worth documenting — right then, while you're in it. Six months from now, your team will thank you. Watch the full IT Leadership Lab session.

Learn more: speedscale.com.

SRE Lead Ricard Bejarano (Cisco) and Jorge Lainfiesta (Rootly) sit down to talk about a recent intermittent incident that had the team scratching their heads.

QA Lead at Rootly explains how she's embraced AI to keep up with the pace of AI-driven feature development.

AI looks great in a demo. The real test is production. In this week's Zero Ticket Minute, Ian explains why success isn't about what AI can do. It's about what it can reliably resolve.

Stop guessing whether your repos meet your branch policies. Start knowing. In this Feature Friday, Senior Engineering Manager Gabriel walks through Cortex's new native support for GitHub branch rule sets and how to use them in scorecards to enforce consistent policies across all your repos. What you'll see: Questions? Reach out to your CSM or drop a comment below.

Replace "AI shipped on hope" with an operating model that holds up once real users depend on it. AI quality is multi-dimensional, covering accuracy, tone, safety, and faithfulness to user data, and can't be debugged from outputs alone. Without visibility into what their AI actually did in production, teams miss regressions, reverse-engineer chains by hand, and watch a single bad answer erode trust built over hundreds of right ones.