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.