Operations | Monitoring | ITSM | DevOps | Cloud

Building Automated Document-to-Video Workflows for Enterprise Operations

In enterprise environments, the volume of documentation is staggering. An average Fortune 500 company maintains hundreds of thousands of documents across HR policies, engineering specifications, sales playbooks, compliance guidelines, and customer support knowledge bases. This content represents a massive investment in institutional knowledge, but its impact is limited by a persistent delivery problem: people do not read documents.

Anatomy of the AI Software Factory: The Context Layer

This is Part 2 of the AI Software Factory series. In Part 1, we established that the Agile methodology is buckling under the weight of “elastic code.” When AI agents can generate functionality in seconds, two-week sprints and manual task management become organizational bottlenecks. We introduced the concept of the AI Software Factory: a shift from managing human tasks to managing business intent through a “Funnel of Increasing Trust.” But a factory requires infrastructure.

From Traffic Context to Confirmed Fix in 3 Minutes

We’ve been building an AI agent that can take a production bug, find the root cause in captured traffic, write a fix, and validate it before a human reviews it. We call it Agent Factory. Last week we ran it on ourselves, against a real bug in our own production service. The first thing we did was get the workflow wrong.

Server Monitoring: The Complete Guide to Metrics, Tools, and Best Practices

If you run IT operations, you already know servers carry most of what your business depends on: When a server slows down or goes offline, the impact spreads fast, and the team feels it before the dashboard does. That's the core problem server monitoring is built to solve. It watches the health and performance of your servers continuously, so issues get caught early instead of becoming outages. The cost of getting these wrong keeps climbing.

Replace Verizon Email-to-Text with OnPage's Paging / Critical Alerting Capabilities

It’s 2:00 AM on a Saturday. An energy company’s thermal storage system temperature violently spikes past safe operating thresholds. The monitoring system instantly fires off an emergency alert via a standard Verizon email-to-text gateway. But instead of waking the engineer, the message is delayed by the carrier network. By the time the on-call responder sees the text hours later, the equipment has failed, resulting in catastrophic downtime.

The Hidden Cost of Kubernetes: Why Your Cloud Bill Is 40% Higher Than It Should Be

The average enterprise running Kubernetes wastes between $2 million and $10 million annually — not from overspending, but from under-optimizing. This is the story of costs you can't see on your dashboard but that your CFO feels every quarter.

DORA Metrics in the AI Era: Why Deployment Isn't Faster

DORA metrics in the AI era reveal a paradox: PR volume is climbing, but deployment frequency is staying flat. In this talk, GitKraken's Director of Product Jeff Schinella breaks down why AI-accelerated code generation is creating a review bottleneck that your DORA metrics can't fully explain on their own. Jeff walks through how PR metrics (cycle time, first response time, code churn, and PR size) serve as the leading indicators behind your DORA data. If your deployment frequency is flat while PR counts go up, the bottleneck isn't your devs. It's your review capacity.

Cribl Notebook templates in Cribl Search

Investigations are time-sensitive, and analysts shouldn’t waste time recreating the same workflows or rewriting familiar queries. Whether troubleshooting infrastructure, investigating suspicious IPs, or analyzing host activity, teams often rely on duplicating old processes and copying query snippets — a slow, inconsistent approach that’s hard to scale.