Operations | Monitoring | ITSM | DevOps | Cloud

Participants are welcomed to a session titled "Observability Simplified," focusing on user experience, application performance, and network behavior. This second part of a three-part series highlights how the Splunk Observability Cloud and Cisco ThousandEyes can create a unified view of applications, infrastructure, and network performance. Key discussions include addressing siloed troubleshooting, enhancing visibility, and a live demo showcasing how to identify network issues affecting application performance. Attendees are encouraged to participate in the Q&A and are reminded that the session will be recorded for future reference.

At hyperscale, a regional cloud outage is not merely a technical disruption—for Samsung Account, which serves 2.1 billion users across three global regions, it is an immediate global service crisis. Fragmented, region-siloed monitoring creates blind spots that make early detection nearly impossible, leaving SRE teams perpetually reactive rather than predictive. The path to proactive reliability requires both a philosophical shift and a foundational change in how observability data is collected, unified, and reasoned over.

Every team is doing something with AI right now. What that something is, is an entirely different question. And whether that something is successful? Most teams are still figuring it out as they go.

AI coding tools are accelerating development velocity, creating a release challenge most teams aren’t equipped for. Without controlled rollout, higher change velocity makes it harder to know which specific release drove the results you’re seeing in production. And when teams use AI, to build AI – LLM apps and AI agents– complexity multiplies. Traditional observability can’t ensure AI agent quality, performance, and cost-efficiency at production scale.

Modernizing a legacy system serving 20 million devices without users noticing is like replacing a jet engine mid-flight. In this session, YoungJin Jung and Donggen Hong from LG U+ share their 18-month journey transforming a Telco-scale API Gateway from a rigid, proprietary solution into a high-performance, open-source architecture on AWS, and the operational challenges they solved along the way.

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.