Operations | Monitoring | ITSM | DevOps | Cloud

#claudecode #optimization #aicoding #observability

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.

Average throughput across all projects on CircleCI increased 59% YoY.

Matt Zytaruk, VP of Technology at Minga, on what changed once his developers could point their AI tools at their own infrastructure. Minga checks in 1.5 million students across the eastern seaboard every school morning. They run that platform on Control Plane, which gives their team an MCP server, a CLI, and Terraform-native resources — so a developer can investigate an issue and generate the code for the change without waiting on anyone.

Give a model permission to act on your computer and you're trusting it will behave the way you expect. It might not. In this clip from our Braintrust conversation, Adam Berman, engineering leader at Semgrep, breaks down why a backdoored model is a different threat model than backdoored software. You can't fuzz-test your way to finding it, and you often can't detect it until it's already acting the way it shouldn't.