Operations | Monitoring | ITSM | DevOps | Cloud

Most AI agents fail in real operations. Too many systems. Too many edge cases. If it needs babysitting, it’s not ROI. Zero Ticket means automation that resolves work end to end.

AI isn’t about replacing engineers. It’s about leverage. The teams that win will be the ones that: Triage incidents faster Correlate signals automatically Reduce manual investigation Automate repetitive operational work In observability, that means asking: AI won’t eliminate expertise, it amplifies it. The real risk isn’t AI taking your job. It’s competitors using AI to operate at a speed and efficiency you can’t match.

Your team's best ideas shouldn't get lost between the doc, the ticket, and the meeting recording — but that's exactly what happens when AI lives outside your workflow. Teamwork Collection by Atlassian puts AI agents and your team on the same page — literally. Ideas flow to execution, agents pull context from your actual projects, and the line between "human work" and "AI work" starts to disappear.

Learn how to implement end-to-end monitoring and observability for Anthropic (Claude) API-based applications using OpenTelemetry and SigNoz. In this video, we walk through instrumenting your Anthropic API calls, collecting traces, metrics, and logs, and visualizing everything in SigNoz to gain real-time visibility into performance, failures, and bottlenecks. You'll see how to move from basic logging to production-grade observability, so you can debug faster, optimize latency, and confidently run Claude-powered AI systems at scale.

Your engineers don't suddenly become less valuable because they hit a token cap.

This is an excerpt from a real AI team weekly meeting where we talk about the stuff we build and occasionally also demo them! In this one, Staff Product Design Engineer Ben Darlow demos improvements to Workspace Home. Staff Software Engineer Sonia Aguilar and Principal Software Engineer Sven Großmann also demo a new dependency graph view for Investigations. We're showing parts of our team meetings to build in public in some small way and give you a sneak preview of what's to come. But not all features we show may make it to production! You've been warned. :)

Government payment fraud is a fast-growing risk for public sector organisations in Australia and globally. From welfare and healthcare payments to business grants and disaster relief, increasingly sophisticated organised criminal networks and other actors exploit complex, high-volume government programs to unlawfully access public funds. The impact is significant—billions lost, program integrity undermined, and essential resources diverted.

Next.js applications can be challenging to debug in production. It’s not always clear where an issue originated or how it impacts users. Hydration errors, server component failures, and performance bottlenecks don’t always come with clear answers.

Spin up a GPU workload cluster using Konstruct's new GPU cluster templates, deploy a self-hosted LLM, and use it in your organization — all live on stream. This hands-on session shows how shipping AI workloads to GPU clusters is just as easy as deploying to Konstruct physical or virtual clusters, and how open source apps in the GitOps Catalog make it even faster. Walk away knowing how to cut your token spend by running models privately on your own infrastructure.

Most cloud providers think AI is just a hardware problem. They focus on the GPUs, the racks, and the raw compute, but they leave the strategy up to you. At Civo, we do AI differently. We don't just provide the hardware; we guide you through the full life cycle of AI adoption, from initial planning to scaling production workloads. By leveraging best-in-class NVIDIA models and GPUs, we give you the performance to unlock AI at scale without the fear of being bogged down by complexity. It's more than infrastructure, it’s cloud freedom with AI built-in.