Operations | Monitoring | ITSM | DevOps | Cloud

Panel: Lessons from the front lines of Observability with Nexi Group

Roman, Principal Architect at Nexi Group — one of Europe's largest paytech companies — shares how his team built observability culture from scratch. Covering AI-agent-led investigation, Adaptive Telemetry cost control, breaking down team silos across.NET and Java developers, and the cultural shift from centralised monitoring to team-owned on-call. Plus: the single most important piece of advice for anyone starting the observability journey.

Metrics overview | Grafana Cloud

Grafana Cloud Metrics helps teams monitor their systems and manage growing metric volume without losing the detail they need to understand what’s happening. As applications and infrastructure grow, so do the metrics teams collect, query, and pay for. Grafana Cloud Metrics is a fully managed metrics service, so Grafana handles the infrastructure and maintenance while your team uses familiar dashboards, queries, and alerts to monitor services and troubleshoot issues.

MySQL slow query and error logs on one OpenSearch dashboard

The MySQL pack in the Logit.io Dashboard Library installs one OpenSearch Dashboards overview, Logit.io MySQL log overview, that reads your MySQL error log and slow query log together on filebeat-*. It shows whether database logs are still arriving, whether slow queries or server warnings are rising and the newest slow statements with their duration, host, user and rows examined.

AWS ECS application log dashboards from CloudWatch Logs

The AWS ECS application logs pack in the Logit.io Dashboard Library gives you six OpenSearch Dashboards for ECS task logs that reach your Logs stack through CloudWatch Logs, covering log volume, application errors, requests, HTTP errors and slow requests, log groups and tasks, plus a log investigation timeline. Every panel reads logGroupName and logStreamName on logstash-* documents, so your forwarder has to keep both fields.

Enterprise Video Platform Buyer's Guide 2026: 12 Platforms Scored on Security, Identity, and Cost

Every enterprise video platform comparison eventually sends a buyer to a sales call, and this one is no exception. That's not a red flag, custom quoting is standard practice across enterprise video hosting plans. What differs sharply is how much a buyer can learn before that call: four of the twelve platforms compared here, Dacast, SproutVideo, Mux, and Gumlet, publish a real self-serve ladder that runs to a top tier; five more publish only a free or entry-level plan before gating the rest; and three, Brightcove, Kaltura, and Panopto, publish no number at any tier.

How do you monitor SAP Cloud Integration before iFlows fail?

SAP Cloud Integration monitoring works best when it looks beyond the failed message list. Confirm that every artifact is deployed and started, compare failures and processing times with each flow’s own history, and track the keyStore, JMS broker, data stores and variables. Those signals usually change well before an iFlow stops a business process.

The myth of multitasking: Why doing more at once doesn't mean getting more done

We've all done it. Answering an email while attending a meeting, checking messages while preparing a presentation, or switching between multiple tasks in an attempt to get through the day's workload. Multitasking often feels like the most efficient way to work. After all, why finish one task when you could supposedly finish three? But there is a catch. What we perceive as multitasking is often our brain rapidly switching its attention between activities rather than performing them simultaneously.
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Your Observability Stack Found the Fire. Congratulations.

The dashboards are red, the alerts are firing, and Slack has officially become a war room. Someone has asked, "Anyone else seeing this?" and 14 people have immediately responded with screenshots. Welcome to another day in distributed systems. The recent GitHub outage is a great reminder of how complicated modern applications have become. Services talk to services, infrastructure scales up and down, retries multiply traffic, and dependencies behave in ways nobody expected. Thankfully, we have observability.

Explaining Atlassian AMP in Three Sentences

This AI Cast episode explains how Atlassian AMP, an agentic multiplayer protocol, should be described to customers. It compares human collaboration in shared Confluence Live pages with two AI agents that check Jira delivery dates or improve wording, while still tracking who did what, for whose behalf, and what they were asked to do. AMP is presented as a set of technologies and patterns that make agent identities, contributions, progress updates, context, permissions, and ways to guide or stop work clear and understandable.

AI agents belong on your Kubernetes platform

In Kubernetes, pods get a new IP every time they restart, so an IP can’t tell you which workload it is. Platform teams learned to identify workloads by labels and identity instead. AI agents are now teaching them a second lesson: an agent’s code can’t tell you what it will do. I believe AI agents belong on the Kubernetes platform. Not beside it, not around it, and not left to each team to secure alone. On it: identified, governed and contained like every other production workload.