Operations | Monitoring | ITSM | DevOps | Cloud

Internet Performance Monitoring detected the AWS outages before AWS acknowledged them. Learn why independent, outside-in monitoring is critical to faster incident response.

Legacy systems, cloud growth, and AI are reshaping government IT. See how connected visibility helps teams respond faster and keep essential services running.

When you get paged at 3am, it takes about 30 seconds for the notification to reach you and maybe two minutes until you're in front of a laptop, awake enough to read. What you see then is usually a raw alert. A metric name, a threshold, a link to a dashboard. Then the ritual starts: open the dashboard, check what deployed in the last few hours, grep the logs for the first error, ask in Slack whether anyone touched the database. ‍ Most of that time is search.

One of the features I have been wanting in Kepler for a long time was the ability to use Pi as my agent when spinning up tasks. Pi is such a minimal harness that it doesn’t prompt for approval for every little thing and it’s system prompt let’s the model just be itself. That minimalism comes at a cost, though. Pi doesn’t have ACP support out of the box, so that means we haven’t been able to officially support it in Kepler, yet.

When something breaks in production, the questions that matter most are also the toughest to answer from metrics alone: who was affected, what did they actually see, and is this worth waking someone up for? Answering those questions requires a fuller picture of the issue and its impact on your users. That’s where Digital Experience Monitoring (DEM) in Grafana Cloud comes in.

You started with Ansible, and for a long time it was the only thing you needed. With a handful of playbooks and an inventory file, you got the job done. However, as the fleet grew, runs went from taking minutes to hours, and parts of the inventory were unreachable at any given moment. This is not an Ansible flaw. Push-based configuration and continuous state enforcement are simply two different jobs. This blog post is all about getting you started with a hybrid system.

For teams building AI agents, the feedback loop should already be a familiar idea: watch how the agent behaves, find what needs improvement, ship a change, and measure the result. In theory, each turn builds on the last until the loop becomes a flywheel and your agent is getting more effective with each turn. In practice, many of us are still in reaction mode. A user reports something strange, costs spike, or an eval score drops.