Operations | Monitoring | ITSM | DevOps | Cloud

Running five AI coding agents in parallel across three repositories sounds like a productivity breakthrough. In practice, it creates a coordination headache: conflicting file edits, orphaned branches, and zero visibility into which agent finished what. Parallel AI coding tools exist to solve exactly this, and Kepler by GitKraken gives you one surface to orchestrate every agent from backlog to merged pull request. This guide compares seven tools built for multi-repo, multi-agent development.

It’s 9 p.m. on Sunday, and an engineering manager is pasting PagerDuty counts and Datadog graphs into a slide deck for Monday’s engineering ops review. On Monday, the first incident readout eats half the hour, the VP drops off for another call, and nobody gets to last week’s action items. Two quiet weeks later, someone cancels the meeting because nothing broke.

I remember the good old days, when troubleshooting application traffic often meant I could examine much of the conversation between a client and server in clear text. Not anymore. Today, nearly everything is encrypted. That is obviously a good thing for security and privacy, but it has also created a common misconception that packet analysis is no longer useful because engineers cannot see the application headers and payload.

Engineers write tests for the logic that worries them and let the small details slide. One of our commerce engineering teams now has a scheduled agent that finds the thinly tested files, writes the missing tests, and opens a pull request for review. Nobody decides to skip a unit test. The core logic gets covered, the small details don’t make the cut, and most days that’s a fair trade.

Service mesh observability guide: the metrics Istio, Linkerd and Envoy proxies emit, what ambient mode changes, and why tracing still needs app changes. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.

The reliability of data is crucial when managing files in the cloud, especially for businesses, enterprises, and even the data reliability of your own personal files. In this article, we’ll cover everything about data reliability: what it is, why it’s important, assessing the reliability of data, and cloud storage solutions to keep your data protected, accurate, and complete.

The first minutes of an incident are spent asking questions a monitoring tool can answer but rarely in the form you asked them: which servers are in trouble right now, is it the same thing as last night, what did memory do on the web servers before it started? Each answer is a few clicks away, on a different page.

This September, we hosted a few of our customers at the ilert headquarters in Cologne. The plan was to share where we're heading, have real-life conversations, and give them more room to ask us questions and share their thoughts. ‍ One part of the day was a discussion about on-call culture with some of the greatest minds in SRE (source: they chose ilert as their incident management tool). We asked our customers: what are your top practices for on-call?

AI dashboards use artificial intelligence to surface relevant metrics, generate visualizations, and add context to complex data. Learn how AI dashboards work, how they compare with traditional dashboards, and what they can mean for IT operations.

The best failover solution keeps your application serving users from another location without a manual cutover. The strongest approach is active-active across two or more locations, where every location already serves live traffic, rather than restoring from backups after an outage.