Operations | Monitoring | ITSM | DevOps | Cloud

How should IT leaders approach automation and AI? Where should they start, and how can they drive measurable results without getting caught up in the hype? In this episode of Agents of IT, Fran Fernandez and Zach Austin sit down with Chris Ellis, Senior Technology Solutions Specialist at RICOne, to discuss practical IT automation strategies, agentic AI, service desk transformation, and the journey toward autonomous operations.

Are your employees still waiting hours or days for IT issues to be resolved? Many organizations have invested heavily in ITSM platforms, self-service portals, and chatbots. Yet service desks continue to struggle with growing ticket volumes, rising costs, slow resolution times, and poor digital employee experiences. In this webinar, Resolve and Redington explore why traditional service management approaches often stop at ticket creation instead of ticket resolution, and how intelligent automation is helping enterprises move toward a Zero Ticket IT model.

What are the latest developments in Arm tooling on Ubuntu? In this talk, David explores Arm tooling to analyze and optimize workload performance, and how AI-assisted development using agentic AI and static analysis can accelerate porting and tuning applications for the Arm architecture. About David David Haikney is a Technical Product Director at Arm. He is responsible for Arm Performix, a free performance toolkit that helps developers understand and improve real-world performance on Arm architectures.

Discover how NVIDIA Earth-2 brings open source software and open science to weather and climate forecasting. Niall Robinson (NVIDIA) introduces a new way of making production-ready weather AI fully accessible for organizations to run, fine-tune, and deploy on their own infrastructure: NVIDIA Earth-2.

You shipped your AI app. But do you have all the answers? Do you actually know which model ran, how many tokens it consumed, or why it stopped? This is what LLM observability gives you, and most AI engineers are skipping it entirely. I built an SOS detection app and used OpenTelemetry to get full visibility into every single call. Token usage, model version, finish reason, and cost per call all in one place, standardised across any provider. Check out the OpenTelemetry GenAI docs in the link below; there is a lot more you can track than you think.