A practical webinar on how AI-driven observability shifts IT teams from reacting to outages to preventing them, by surfacing anomalies early, cutting alert noise, and connecting signals across metrics, logs, and traces before small issues turn into downtime.
Most IT teams still find out about problems when users do. This session shows how AI-powered observability changes that. We walk through how correlating metrics, logs, traces, and topology in one place lets teams catch the early signals of failure, separate real incidents from noise, and act before services go down. You will see where AI adds practical value in day-to-day operations and how a unified approach reduces mean time to detect and mean time to resolve. This session shows a better way: bringing metrics, logs, and traces onto one platform, with AI doing the correlation. You'll see how teams cut alert noise, lower MTTR by up to 45%, move from firefighting to prevention, and what comes next as agentic AI starts running more of operations.
Why traditional monitoring struggles in cloud-native environments
How metrics, logs, and traces work together for a full 360° view
How AI cuts alert noise by 30-40% and MTTR by up to 45%
What a true single-pane platform looks like across logs, APM, RUM, and SLOs
Where observability is headed with agentic, self-healing operations
Date
20th July 2026
Duration
1 Hour
Attendees
Global IT & Operations Teams
Level
Intermediate – Advanced
Webinar Agenda
A comprehensive breakdown of topics covered in this session.
A quick look at what we'll cover and who's presenting.
What tool sprawl, data silos, and alert fatigue actually cost your teams.
Why observability tells you why something broke, not just that it did.
How AI cuts noise, catches anomalies early, and points to root cause faster.
A walkthrough of logs, APM, RUM, and SLOs working on a single console.
Where agentic AI and autonomous operations are taking IT, then your questions answered live.