Operations | Monitoring | ITSM | DevOps | Cloud

Real-Time CPU and Memory Insights for Harness CI Cloud Builds | Harness Blog

When a CI pipeline runs on cloud infrastructure, the build machine is ephemeral. It spins up, executes your build, and disappears. During that window, you have zero visibility into how much CPU and memory your pipeline actually consumes. This blind spot creates real problems. Teams over-provision VMs "just in case," wasting compute spend. Others under-provision and deal with silent OOM-kills or CPU throttling — the only clue being a cryptic exit code 137.

Harness Named a Leader in the 2026 Gartner Magic Quadrant for DevSecOps Platforms for the Third Consecutive Year | Harness Blog

Harness has been recognized as a Leader in the 2026 Gartner Magic Quadrant for DevSecOps Platforms for the third consecutive year. Harness was also positioned furthest on the Completeness of Vision axis in the report. Harness has been recognized as a Leader in the 2026 Gartner Magic Quadrant for DevSecOps Platforms for the third consecutive year. Harness was also positioned furthest on the Completeness of Vision axis in the report.

Anthropic Fable 5 & Mythos 5 Suspended AI Risk Revealed!

Your entire AI stack ran on a model that disappeared in three days. The US government issued a directive suspending all access — a few hours' notice, no deprecation window, no roadmap. Launched Tuesday. Gone by Friday. And every enterprise that had built workflows on top of it just found out what the real risk was: not the model itself, but the absence of a governance layer underneath it.

Tokenmaxxing: The AI Productivity Lie

Your best engineer spent 500,000 tokens last week. Nothing shipped. There's a name for it now: tokenmaxxing. Failed prompts, dead PRs, code that never reaches production — it looks like productivity, but it isn't. Most engineering leaders can't tell you what percentage of AI-generated code actually ships, or where the budget went. You should be able to say "that bug cost me $2,700 in tokens to fix.".

Mainframe DevOps: Modern CI/CD for Big Iron | Harness Blog

For Platform Engineering teams, the goal has always been clear: build a secure, scalable internal developer platform that reduces cognitive load and accelerates time-to-market. Yet, a massive obstacle often remains hidden in plain sight: the mainframe. While your distributed teams are shipping cloud-native microservices multiple times a day, your core backend mainframe applications frequently remain locked in an isolated silo, lagging behind on slow monthly or quarterly cadences.

From Commit to Approval, Without Leaving VS Code | Harness Blog

The Harness VS Code Extension is now on the Marketplace. Monitor pipelines, debug logs, approve deployments, and query failures with Claude Code, Copilot, or Cursor, without leaving VS Code. Your Harness pipelines, logs, and deployment approvals are now a sidebar panel away inside VS Code. The Harness VS Code Extension is live on the VS Code Marketplace today, no.vsix download, no manual install.

Azure Deployment Strategies & CI/CD Best Practices | Harness Blog

‍ Learn how to master Azure deployment with CI/CD pipelines, progressive delivery, and feature flags. See how Harness helps engineering teams ship faster and safer on Azure. Azure deployment sounds straightforward. Push code, it runs in the cloud. But if you've managed a 2 a.m. production incident because a deployment went sideways on AKS, you know the gap between "it deploys" and "it deploys safely at scale" is significant.

AI Cost Savings Unlocking Hidden Engineering Value

Bain says AI cost savings aren't arriving. But the value isn't missing, it's invisible. Most engineering teams can see token spend. They can see AI usage. What they can't see is whether any of it shipped, and whether it moved the needle on delivery. That's the measurement gap. And until it closes, AI ROI will keep looking worse than it should.

Enforce Artifact Governance with OPA Policy-as-Code | Harness Artifact Registry

Artifact governance should not depend on manual checks. But for many teams, container images, software packages, and open-source dependencies are imported into registries from multiple internal and external sources. Without automated guardrails, vulnerable images, untrusted packages, end-of-life dependencies, or non-compliant artifacts can reach developers and delivery pipelines.