Operations | Monitoring | ITSM | DevOps | Cloud

Observability looks perfect in a slide deck – in practice, it's messier. In this panel, engineering leaders from Cyara, PlayHQ, and National Australia Bank share what really happened when they scaled observability: unexpected cloud bills, alert fatigue, a weekend database outage caught by an AI-assisted triage agent, and a vendor dispute settled by a single chart. They also cover moving beyond legacy tooling, using AI to close the PromQL skills gap, and what's next – from agentic SDLC integration to continuous profiling. Real stories, real numbers, real lessons.

Many times, it feels like you're maintaining multiple versions of the same dashboard (with a slight modification), *OR* simply spending more time writing queries rather than actually looking at the actual data? In this Campfire community call, we're taking a deep dive into two things that are reshaping how Grafana dashboards get built: Dynamic Dashboards and the Grafana AI Assistant and showing you how to combine them to go from a blank canvas to a reusable, production-ready dashboard in minutes.

When should you use gcx? If Grafana Assistant is the brain and Grafana MCP is the easy hand, gcx is the power hand. Built for AI agents working in the terminal, gcx gives them deep access across Grafana Cloud—so they can pull telemetry, verify code, automate workflows, and access places MCP doesn’t. Coding agents? gcx. Need the full Grafana Cloud surface? gcx. Automating in CI/CD? gcx. Here’s where it fits, and when to use it — explained by Nicole van der Hoeven.

When should you reach for Grafana MCP? It’s one of the two “hands” in Grafana’s AI toolkit — and the easy one at that. MCP lets you bring Grafana into the tools you already use, like ChatGPT, Claude, or Cursor, without changing your workflow. No terminal? MCP. Want to stick with your favorite AI tool? MCP. Want easy tool discovery out of the box? MCP. Here’s where it fits, and when to use it — explained by Nicole van der Hoeven.