Operations | Monitoring | ITSM | DevOps | Cloud

Why Today's ITOps Workflows Break When Systems Get Too Big

Modern, hybrid environments change continuously. But, legacy ITOps workflows assume stable infrastructure. IT environments don’t behave in predictable ways. Infrastructure changes continuously, services spin up and shut down on demand, and data formats evolve with every deployment. Most ITOps workflows, however, are still designed around the assumption of stability. That mismatch drives failure. Static runbooks expect environments to stay put.

What We Built in 2025, and Why It Matters Going Into 2026

As we move further into 2026, we wanted to pause for a moment and reflect on what the past year looked like for OnPage, not just in terms of features shipped, but in how the platform evolved to better support the way teams actually work in high-stakes environments. 2025 was a foundational year for us.

Building reliable dashboard agents with Datadog LLM Observability

This article is part of our series on how Datadog’s engineering teams use LLM Observability to iterate, evaluate, and ship AI-powered agents. In this first story, the Graphing AI team shares how they instrumented their widget- and dashboard-generation agents with LLM Observability to detect regressions and debug failures faster. Visibility into how large language model (LLM) applications behave in real time is essential for building reliable AI-driven systems at Datadog.

Elevating global operations: Mastering multi-cluster Elastic deployments with Fleet

In today's global enterprises, distributed infrastructure is the norm, not the exception. Organizations operate across continents and are driven by customer proximity and regulatory requirements. For the Elastic Stack, this reality often translates into a multi-cluster deployment model, where data is collected and stored in multiple geographically dispersed Elasticsearch clusters. But, why adopt complexity? The decision to decentralize data storage is generally driven by three critical factors.

Easy Guide for Connecting VictoriaMetrics to a Grafana Data Source

VictoriaMetrics is a fast, cost-efficient, and highly scalable time-series database designed as a drop-in replacement for Prometheus storage. It is widely used for collecting, storing, and querying metrics at scale, while remaining lightweight enough to run as a single binary or container. Because it is fully Prometheus-compatible, VictoriaMetrics supports standard PromQL queries and integrates seamlessly with Grafana.

Why agentic AI is the future of IT change management

Every enterprise depends on continuous changes to its IT environment. New code releases, infrastructure updates, configuration changes, and security patches are all crucial to support continuous innovation. These same changes are also a leading source of operational risk and one of the most common causes of failures at the network, infrastructure, and software layers, resulting in outages.

How the Right Business Essentials Support Long-Term Efficiency

Running a business smoothly depends on many small details. One of the most important things is having the right supplies to do daily work. If people don't have what they need, tasks slow down, and problems pile up. And efficiency - the ability to get things done well and on time - suffers. Well, it's worth noting that workplace essentials aren't glamorous. They're not flashy. But they are the foundation of daily operations. When these basics are reliable, teams can focus on real work instead of scrambling for tools or replacing worn-out items.

What to Plan Before a Full Home Renovation Starts

A full home renovation can feel exciting at first, and then quickly turn into chaos if you jump in without a clear plan. The good news. Most renovation stress comes from the same few problems: unclear priorities, messy decisions, and unrealistic timing. If you handle those early, the rest becomes much easier to manage.

How AI OCR Is Reshaping Automated Data Extraction in Large-Scale Business Operations

Businesses handle massive amounts of data every day. Such data is obtained from invoices, bills, contracts, applications, and many other documents. Most of these documents are distributed in the form of scanned copies and images. As a result, whenever organizations resort to manual data entry in processing such data, the process turns out to be slow and filled with errors. However, to avoid these issues, organizations are now turning to AI-OCR solutions for better data extraction and increased operational efficiency.

AI in Contact Centers: Capabilities, Limits, and the Missing Decision Layer

AI in contact centers refers to the use of artificial intelligence technologies to automate customer interactions, support agents in real time, analyze conversations, and improve operational efficiency. In practice, this includes chatbots, virtual agents, intelligent routing, agent assist tools, sentiment analysis, and automated quality assurance systems designed to increase speed, consistency, and scale.