Operations | Monitoring | ITSM | DevOps | Cloud

Most engineering leaders are drowning in data but starved for insight. We have dashboards full of metrics, but they often create more questions than answers and rarely tell us what to do next. In the age of AI, where development velocity is accelerating at an unprecedented rate, this problem is only getting worse. Shipping code faster than you can fix it is an existential risk, and a dashboard that doesn't lead to action is just a distraction.

Microsoft forces unlicensed OneDrive accounts read-only at 60 days, archives them at 93, and deletes at 365 unpaid days. The timeline, the fees, and how to avoid it.

AI SRE is making real strides in resolving some of the greatest pains related to incident response, troubleshooting, and complex root cause analysis. The on-call rotation, the war room, the week-long RCA, and the ticket queue that ate a third of every platform engineer’s week all look different now than they did two years ago.

When spend moves, the first question is always “compared to what?” Answering it used to mean pulling up two tabs with two different date ranges and toggling between them, or squinting at a single delta number without the ability to customize what you’re comparing against. Compare Periods puts both periods in front of you at once, so a spike, a regression, or a shift shows up on the chart and in the cost table, without you doing the math yourself.

AI cost monitoring is the continuous tracking of AI and LLM spend in real time, broken down by the models, features, teams, and customers generating it. It is not the same as reading the monthly bill - done well, it shows spend as it happens, flags anomalies before they become invoices, and connects every dollar to an outcome so finance can protect AI ROI instead of explaining it after the fact.

Agentic AI cost is what you pay to run AI agents, and it is mostly tokens. An agent does not answer once. It loops, calls tools, reads the results, and reasons again, re-sending a growing context every step. Anthropic found agents use about 4x the tokens of a chat, and multi-agent systems about 15x. You control it by capping runs, right-sizing the architecture, routing, caching, and measuring cost per task, then tying every agent to the AI ROI it produces.

In May I wrote that you probably don’t need private PKI for internal infrastructure, because DNS validation gets a publicly trusted certificate onto hosts that never touch the internet. In June I pointed out that Apple enforces an 825-day cap on private certificates, so your own CA doesn’t even free you from the browser vendors. Two days ago I told you that public client certificates stop renewing in October, and that the replacement is a private certificate authority.

If you haven’t heard about our MCP server, you should really check it out. It’s probably the best way to give your agents access to the power of GitKraken’s integrations and features. Our MCP tools also help your agents understand your codebase in a way that we think lowers your token usage and improves their output.

Most PR descriptions leave out the one thing a reviewer actually needs: why the change was made. And AI review bots that live natively inside GitHub tend to solve that with noise instead, dropping comments a reviewer then has to sort through to find the two that matter.