Operations | Monitoring | ITSM | DevOps | Cloud

At Grafana Labs, observability is what we do. So as we started building AI agents, we naturally reached for the same instincts we bring to every system: measure it, set targets, and make reliability something you can reason about instead of hope for. That instinct led us somewhere unexpectedly useful. It turns out one of the oldest ideas in reliability engineering, the error budget, maps beautifully onto one of the newest problems in software: how do you know if an AI agent is actually any good?

EU buyers asking for Hosted OpenSearch with data residency usually mean something precise: indexes and cluster storage should land in a European data centre that lines up with GDPR expectations and the geography named in the DPA — not a US default that security later has to unwind. On Logit.io that choice is an account-level data storage region, not a free toggle on every stack. Get the first stack right and every later OpenSearch or log stack in that account follows.

Why does a switch port look healthy on every dashboard while people on that floor keep reporting dropped calls and slow file transfers? Often the cause is CRC errors, a port counter that most dashboards do not show by default. Each CRC error means a unit of data arrived damaged and the switch discarded it. Put simply, a CRC error tells you the receiving device noticed the data changed somewhere in transit.

Most bandwidth problems get investigated after the complaints arrive, and by then the traffic that caused them has moved on. The numbers you need sit in several places at once. A laptop knows its own traffic. The router knows what leaves for the internet, and only the switches see how much network bandwidth moves inside the building. Checking bandwidth usage at the right layer saves hours of guessing. Most of the methods ship with hardware you already own.

Enterprises are running AI agents nobody has counted, holding credentials nobody reviewed, taking actions nobody can audit. AgentIQ governs what agents are allowed to do at the moment of action—inside the execution flow—not after the fact through a gateway watching from outside.

TypeSafe AI released Jev in September 2026 to do one thing: make decisions. Give it a state (a string or a JSON object) plus a set of typed questions, and it returns typed answers with probabilities. It never explains itself, and that constraint is the whole idea. Evaluation pipelines have spent the last two years asking text generators for yes/no verdicts, wrapping the reply in a JSON schema, and paying generation prices for what amounts to a single bit.

How do you know a server is about to cause problems before it actually does? You track the right metrics. Not all of them, just the leading ones that consistently surface performance issues before they worsen into outages. This guide breaks down the 10 server performance monitoring metrics every SRE should have on their radar.

Most Azure monitoring setups look the same: Azure Monitor for metrics, Application Insights for apps, Log Analytics for logs, a separate tool for network, and another for cost. Each works in isolation. None of them talk to each other when something breaks. The Azure monitoring tool in ManageEngine OpManager Nexus consolidates infrastructure, application, network, log, and cost visibility data into a single console.

There is a peculiar moment that happens when talking to AI. You ask it to rewrite an email, it does a good job, and you type, "Thanks!" Then, almost without thinking, you add, "Sorry, one more thing." It is software. It cannot be kept waiting, interrupted, or offended. Still, somehow, you have developed the manners. Then the questions get a little more personal.

Effective S/4HANA migration monitoring closes the operational gaps that quietly undo complex SAP transitions. Avantra eliminates the seams between phases where visibility typically disappears exactly when it matters most: the shift from baseline to cutover, the blind spot inside a parallel run, and the rushed handoff from legacy tools to Cloud ALM. This guide walks through every phase of migration monitoring in order, with a checklist you can adapt to your own project.