Operations | Monitoring | ITSM | DevOps | Cloud

The pace of AI-assisted development has outgrown how most teams actually ship. Harness is closing that gap. Engineering teams are generating more shippable code than ever before — and today, Harness is shipping five new capabilities designed to help teams release confidently. AI coding assistants lowered the barrier to writing software, and the volume of changes moving through delivery pipelines has grown accordingly. But the release process itself hasn't kept pace.

Discover why Terraform has become a vendor lock-in risk with HashiCorp's licensing changes. This guide explores practical strategies to escape Terraform lock-in, including OpenTofu migration paths and how Harness IaCM enables vendor-neutral infrastructure management across multiple IaC tools.

Here's a scenario that probably sounds familiar: a developer needs a sandbox environment to test something. They file a ticket. Then they wait. And wait. Maybe a day goes by, maybe three. Meanwhile, your platform team is buried in provisioning requests, and somewhere, someone has already spun up an unsanctioned workaround that bypasses every governance policy you've put in place. It's a lose-lose. Developers lose velocity, platform teams lose their sanity, and security gaps quietly multiply.

The catastrophic TeamPCP exploit in March 2026 demonstrated that "open execution" models, in which third-party code runs with full privileges, have made CI/CD pipelines a primary target for global credential harvesting. There are better architectures. On March 19th, the risks of running open execution pipelines — where what code runs in your CI/CD environment is largely uncontrolled — went from theoretical to catastrophic.

This comprehensive guide covers the fundamentals of load testing, key differences from stress and performance testing, step-by-step execution methods, popular tools, and best practices to help teams build resilient systems with confidence. In today's always-on digital economy, a single slow page or unexpected crash during peak traffic can cost businesses thousands or even millions of dollars in lost revenue, damaged reputation, and frustrated customers.

Taking a look back over the last ten years in enterprise technology, paradigm shifts are occurring more frequently. For example, the maturity of DevOps/Platform Engineering and Cloud Native infrastructure has occurred. The new frontier depending where you are in adoption is AI. As your adoption and maturity curve progress, operationalizing these paradigms become important.

For the past few years, the narrative around Artificial Intelligence has been dominated by what I like to call the "magic box" illusion. We assumed that deploying AI simply meant passing a user’s question through an API key to a Large Language Model (LLM) and waiting for a brilliant answer.

On March 24, 2026, the AI open-source ecosystem was impacted by a critical supply chain attack involving the widely used Python package LiteLLM. Attackers compromised the LiteLLM PyPI distribution pipeline and published malicious versions (notably in the 1.82.7-1.82.8 range), embedding a multi-stage payload designed to steal credentials and execute remote code.

You're tagging Docker images with build numbers. -Build is your latest production release on main. A developer pushes a hotfix to release-v2.1, that run becomes build. -Another merges to develop, build. A week later someone asks: "What build number are we on for production?" You check the registry. -You see,,, on main. The numbers in between? Scattered across feature branches that may never ship. Your build numbers have stopped telling a useful story.

--- Key Takeaway: Harness AI helped deflect 95% of the platform support tickets for a major financial institution --- These days, success is often measured by what doesn’t happen: When things go right, the software delivery platform is invisible. But what happens when an organization’s delivery velocity increases multifold? Can the platform still stay out of the way?