Operations | Monitoring | ITSM | DevOps | Cloud

If your team builds with AI, someone is defaulting to the biggest model available (say, Fable) because it feels like the safe pick, and the safe pick is almost always the most expensive one. One over-powered choice looks harmless on its own, but multiplied across every prompt, agent, and workflow, and you get a big number on the P&L. All that, yet nobody chose which model on purpose. As we like to say, using a default is not a decision.

AI cost reduction means lowering what you spend to run AI (tokens, inference, and compute) without sacrificing quality. The highest-leverage tactics, prompt caching, batching, and routing easy work to smaller models, cut spend 50 to 90% by removing waste, not capability. Somewhere right now, a finance leader is opening an AI bill that has quietly tripled, with no new product to show for it. Nobody approved it. No single decision caused it.

Hundreds of files vanished from SharePoint. How to tell ransomware or a wipe from a user re-filing a folder, using the signals in the audit log that actually separate them.

Harness shipped 71 features in July, about one every 10 hours. That's more than June's 62, and the surge lines up with what AI is doing to the rest of the SDLC: coding agents are writing more of the code, test agents are now generating and running more of the tests by default, and every stage downstream: deployment, security, cost, and resilience has to absorb that pace without falling over.

As organizations ship software faster than ever, runtime behavior changes are becoming just as frequent as code releases. Teams need a way to update application behavior without waiting for code deployments while maintaining visibility, governance, and control. ‍ Now available in beta, Config Management provides a governed runtime control plane that separates runtime configuration from application deployments, enabling organizations to deliver configuration changes instantly across environments.

For many organizations, digital employee experience (DEX) is still viewed as an IT project. It measures endpoint health, identifies performance issues, and helps service desks resolve incidents faster. While that’s useful, it’s also far too small a vision. In order to get the greatest return from DEX, organizations have to stop treating it as exclusively an engineering capability and started treating it as an equally powerful intelligence capability.

This month is about shortening the path from detecting a problem to fixing it. You can now create GitHub or Linear issues directly from Scout, Node.js now has complete logs and error monitoring, and Anomaly Detection gets more control over what actually triggers an alert. Here’s what’s new in July.

We’re proud to share that the Progress WhatsUp Gold solution has been recognized as a Leader in the QKS Group SPARK Matrix: Network Observability report, ahead of other vendors such as SolarWinds, Paessler PRTG and LogicMonitor to name a few. The recognition highlights the WhatsUp Gold network monitoring capabilities that help organizations gain deeper visibility into complex network environments while delivering impactful benefits for customers.

Every so often we sit down with someone from our support team and turn their week into a blog post. First up: Roberto Vizcarra, on four things generating tickets lately, AI credits, student plans, integrations, and Mac performance. Here’s what changed and what to do about it.

Slow application complaints are among the hardest tickets in IT to close. The network gets blamed first; the dashboard shows nothing wrong, and the ticket bounces between teams for a week. Network latency sits at the centre of that argument more often than any other metric. Most dashboards report latency as a single average, and that average hides the slow requests people actually notice. A path can average 30 ms and still drop a call every ten minutes.