Operations | Monitoring | ITSM | DevOps | Cloud

Engineering teams are great at innovating and delivering products, but the work that's required to maintain them over time and keep them running well tends to get deprioritized. Planning processes are designed to move features forward, not to catch whether those features are generating too many alerts, degrading in performance, or creating compliance exposure over time. As a result, that class of work accumulates quietly.

DCIM software supports edge computing, IDF closets, and distributed IT environments by providing centralized asset management, real-time power and environmental monitoring, 3D digital twin visualization, capacity planning, and physical security management across every site from core data centers to remote sites and IDF closets.

Spreadsheets and Visio diagrams may work in small, static environments, but they cannot maintain accurate, real-time records at the port level, track relationships between assets, or support the pace of change in modern operations. DCIM software is purpose-built for those demands. In this blog post, we'll cover what network documentation actually requires, where Excel and Visio fall short, and how DCIM software addresses those gaps.

E2E Testing Has a New Bottleneck, and It's Not the Code End-to-end (E2E) testing has always been the hardest part of a QA strategy. You're simulating real users, navigating real flows, validating real outcomes across browsers, environments, and data states that never hold still. Traditional test automation tackled this with scripts: rigid, deterministic sequences tied to element selectors and hard-coded values. They worked until the UI changed. Or the data changed.

Outages in distributed systems are inevitable, making resilience testing essential in the SDLC. It must be continuous, covering failures, load, and disasters. Delayed validation creates “resilience debt,” increasing risk. A holistic approach—combining chaos, load, and DR testing—plus cross-team collaboration and AI-driven insights improves reliability and reduces impact. Modern software delivery has dramatically accelerated.

Summary: A test hook connects a test or lint command to an event in your AI coding agent’s workflow. When the event fires, the agent runs the command automatically. If it fails, the agent’s action is blocked. You can wire your existing test commands into your agent’s lifecycle hooks to get deterministic local validation before code ever reaches CI. AI coding agents write code at a pace where stopping to manually run tests breaks your flow.

For the first time in a decade, humans are the minority on the open web. In 2025, automated traffic officially crossed the Rubicon to account for 51% of all web activity, while generative AI-driven referrals to retail sites surged by a staggering 693% year-over-year. As we move through 2026, these are no longer just "bot" statistics to be handled by a WAF. They represent a fundamental shift in user behavior. The fastest-growing segment of your audience is now agentic.

Every incident platform needs to know who owns what. Which team owns which service. Which backlog to send follow-ups to. Which escalation path to page when something breaks. The problem is that most platforms encode this ownership logic separately in every configuration: alert routing, workflows, ITSM ticket syncing, and more. Each one maintains its own copy of the same information, in its own format.

Out-of-the-box and bring-your-own AI agents that encode operational knowledge boost troubleshooting speed and accuracy across cloud native infrastructure TEL AVIV and SAN FRANCISCO, March 18, 2026 — Komodor, the autonomous AI SRE company for cloud-native infrastructure, today announced a new extensibility framework that transforms its Klaudia AI technology into a universal multi-agent platform for troubleshooting and optimizing performance of complex cloud native infrastructures and applications.

A real workflow showing how Claude + CloudZero MCP turns plain-English questions into actionable cost intelligence — no dashboards, no tickets, no waiting As Director of Finance and Accounting at a software company, my job can be described simply: Understand what we’re spending, who’s responsible, and whether we can get more efficient. But as anyone who’s had to wrangle AI costs knows, doing so for AI is anything but simple.