Operations | Monitoring | ITSM | DevOps | Cloud

The contemporary IT landscape for mid-market organizations is defined by a paradoxical pressure: the mandate to accelerate digital transformation and AI integration while operating under the most stringent cost discipline observed in decades. For firms positioned between the nimble agility of startups and the vast resources of global enterprises, the "complexity of data lineage" and "legacy modernization paralysis" have emerged as primary barriers to progress.

For today’s leading enterprise computing environments, the concept of “centralized headquarters” is a relic. Today, R&D happens on different continents, spanning cloud, on-prem and hybrid environments, while stretching across multiple regulatory jurisdictions. But here is the hard truth: Most global organizations are still managing their binaries using legacy mirroring or “blind” infrastructure-level syncing. They treat artifact delivery like a basic file-transfer mechanism.

If you've ever fixed a bug only to watch it come back two weeks later, you already understand why root cause analysis matters. Patching symptoms feels productive - it's not. Getting to the actual cause is what prevents the same issue from eating your team's time over and over again. This guide covers everything you need to know about root cause analysis (RCA) in software testing: what it is, how to do it, which tools help, and where AI is taking it next.

Thousands of organizations rely on open source community builds for infrastructure automation. But if you're tasked with certifying, maintaining, and patching those builds yourself, you know the burden firsthand. The reality is that managing open source internally consumes time, introduces risk, and diverts resources from higher-value initiatives. When critical vulnerabilities emerge, your team scrambles to assess, test, and deploy fixes, all while keeping production environments stable.

AI in Service Management promises faster resolutions and fewer repetitive tickets. In reality, its performance depends on something far less glamorous: a reliable knowledge layer. Many service desks already have the answers; they’re just buried inside resolved tickets instead of organized in a way AI can use.Turning those resolutions into structured documentation takes time that rarely fits into daily operations.

Agentless discovery for Windows devices plays an important role in maintaining visibility across modern IT environments. Why? Because according to data from StatCounter, Windows accounts for about 67% of desktop operating systems worldwide, and its presence is typically even higher in corporate environments. This is where agentless discovery becomes especially valuable.

Harness Database DevOps integrates Open Policy Agent (OPA) to enforce database governance through policy as code. By embedding compliance rules directly into CI/CD pipelines, teams can automatically prevent risky database changes, maintain audit trails, and meet regulatory requirements without slowing down development. Database systems store some of the most sensitive data of an organization such as PII, financial records, and intellectual property, making strong database governance non-negotiable.

Dozens of startups are sprinting to build the next “agentic SIEM” that can autonomously detect, investigate, and respond to threats. They’re well-funded, well-marketed, but structurally hollow. Here’s what it usually looks like: an LLM layer on top of a thin orchestration engine on top of fragmented or customer-hosted data lakes. While it looks impressive in a demo, it quickly falls apart in production. Why? It’s not built on a strong foundation.

This is a guest post from Adrian Tan. Over recent months, we’ve been steadily improving PostgreSQL monitoring in Redgate Monitor, with a singular focus: to help Postgres users diagnose performance problems faster, with less manual investigation. The latest updates and new features tackle this problem in a few different ways.

Since the rise of Large Language Models (LLMs) like GPT-3 and GPT-4, organizations have been rapidly adopting Agentic AI to automate and enhance their workflows. Agentic AI refers to AI systems that act autonomously, perceiving their environment, making decisions, and taking actions based on that information rather than just reacting to direct human input.