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.

#claudecode #optimization #aicoding #observability

AI can accelerate speed to market, but for IT leaders it also raises a harder question: can you prove how an AI-generated change reached production? Chris Yates (SVP, Managing Director of Data & Architecture, Republic Bank) explains how his team builds a full evidence trail for every change, using version-controlled deployment tooling like Redgate Flyway Enterprise, so governance becomes a guardrail rather than a brake on speed.

Datadog Bits Agent Builder lets you build AI agents that use your observability data to automate operational tasks. In this walkthrough, see how to build an agent that analyzes monitor and alert activity, identifies patterns, and provides actionable recommendations to improve your monitoring strategy. With Bits Agent Builder, you can give agents access to Datadog data and tools, customize their instructions and models, and run them automatically to continuously analyze and act on your environment.

Last year, Lumen shared its AIOps roadmap. This year: two updates. First, how Lumen's thinking on AI-era org design has shifted — including why the instinct to cut junior headcount is a trap, and what sustainable team structures look like instead. Second, what Lumen built as a result: a single AI core (AskGreg) extended into a customer-facing email agent (NORA), a chatbot, and an in-progress voice agent — all leveraging the same modular platform. Live video demos show the system diagnosing network problems from alarm data and driving real-time network decisions. The closing thesis ties it together.

AI in network operations has moved past the demo. In some shops it's already cutting detection time and clearing the alert noise that used to bury the engineering team. In others, a promising rollout has quietly stalled the moment it met production. That difference between the two rarely comes down to the model, it comes down to how the AI gets integrated into the network and most importantly, the team around it.