Operations | Monitoring | ITSM | DevOps | Cloud

A green uptime check only proves the server responded, not that customers can actually log in, search, and pay. Synthetic transaction monitoring runs your critical user journeys 24/7 from real locations and alerts your team the moment a flow breaks. Catch it before your customers do.

Instead of waiting for an alert to trigger a long, stressful triage and replication process, catch the issue before it reaches production by replaying real traffic against your code first.

View Kubernetes events in Last9 — across clusters, deployments, statefulsets, and even correlated with services.

Watch the full episode: MCP vs CLI: Does it even make a difference? Live Laugh Logs ep. 3.

See our latest retrospective and live updates. We're showcasing our new UI and navigation, Qoevry Skills and MCP, Copilot Read/Write Mode and much more! See live all the new features as the infrastructure platform for the agentic era!

You already have every error, trace, log, and replay in Sentry. Now let Seer, our agent debugger, do its thing: use that context to find what broke, why it broke, and how to fix it.

What does enterprise AI look like when the user is no longer human? In this episode of The Intelligent Enterprise, host Tom Stoneman sits down with Ash Ashutosh, CEO of Pinecone and a three-time founder, to explore the next evolution of AI infrastructure: from vector databases built for humans using chatbots, to knowledge engines designed for AI agents that need to understand context, take action, and get work done.

Without constraints, feedback, and clear standards, AI can create complexity faster than teams can manage it.

In this session, we’ll show how the Progress WhatsUp Gold solution has evolved beyond traditional monitoring. With built-in Network Detection and Response (NDR), teams can identify suspicious behavior, detect anomalies earlier and respond faster. Combining monitoring and NDR into a single platform gives IT teams a clearer view of their environment and a faster path from detection to action by consolidating tools.

AI coding tools are in use across regulated industries, producing workable code faster than humans can review. It’s a shift that’s raising difficult questions that haven’t been fully answered yet. What does governance look like when AI agents are writing the code, reviewing each other’s work, and fixing their own findings? How do you prove what code is running and whether it passed your checks? Who’s accountable for change approvals, audits, incidents?