Operations | Monitoring | ITSM | DevOps | Cloud

The modern enterprise runs on a technology stack that changes faster than the operating model responsible for keeping it available. Applications that once moved through scheduled releases now change continuously. Infrastructure is distributed and dynamic. Services depend on other services, teams depend on other teams, and operational data arrives from more places than any individual can reasonably inspect. The business asked for agility, flexibility, and velocity, and the tech delivered.

Most teams we talk to are fighting the same battle. The knowledge needed to make a decision already exists somewhere. However, it’s locked in a tool your team isn’t looking at, or in the head of the one engineer who’s seen this same type of incident before. This month’s updates all chip away at that same problem. Here’s what’s new.

Internal hackathons are a powerful way to let your teams explore ideas and work together on something fun besides the same old stuff for work. Maybe they want to build something brand new, maybe they want to knock out things that are on the backlog that never get prioritized, or maybe they want to work on something fun but completely unrelated to work.

Over the summer, Fly.io founder Kurt Mackey announced a complete pivot for the company toward "Computers for Agents", which are ephemeral virtual machines (called Sprites) optimized for AI coding workflows. He was refreshingly explicit about what this means: they are not trying to do both traditional application hosting and AI agent compute. They are choosing one over the other. This is a completely rational bet on the future of developer tooling.

Data center capacity planning concerns are growing. The Uptime Institute Global Data Center Survey 2026 reveals that the share of data center managers and operators who are at least somewhat concerned about forecasting future capacity requirements has risen from 68% in 2024 to 76% in 2026. Data center professionals are caught between accelerating business demands, aggressive artificial intelligence rollouts, higher-density GPU clusters, and intense scrutiny over power and cooling.

What does a typical day look like for the people working behind the scenes of a data centre? From hardware installations and cabling to troubleshooting, customer requests and supporting critical works, every shift brings something different. In this 'day in the life', Leon Strong, Data Centre Services Engineer for Maidenhead, shares what it’s like to work in a hands-on technical role, what they enjoy most and their advice for anyone considering a career in data centre engineering.

Low disk space remediation is the process of diagnosing storage consumption, safely recovering capacity, and confirming that the affected system and its dependent services are healthy. Automated remediation begins when a monitoring or AIOps platform detects a threshold breach and triggers a governed workflow.

Kafka is a crucial and widely used technology: 80% of the Fortune 100 rely on the event streaming platform as part of their stack, according to Apache. But Kafka issues can be complex to manage and even more difficult to troubleshoot, as the same symptom can point to very different problems. Suppose consumer lag on your checkout-events topic suddenly exceeds its SLA.

When Datadog acquires a company, we usually rebuild the product rather than plugging it in as is. That’s exactly what we did with Eppo, an experimentation and feature-management platform. Eppo’s feature-management capabilities became Datadog Feature Flags, while experimentation became Datadog Experiments. This post focuses on the experimentation platform and four changes we made to help you get to a decision faster.

AI agents can help investigate failed builds, fix flaky tests and automate other development tasks. But setting up those agents has required more Pipelines configuration than it should. Agent-powered steps often need different compute, permissions and runtime settings from ordinary build and test steps. Until now, teams have either repeated those settings across every agent-powered step or tried to make one set of global defaults work for everything.