Operations | Monitoring | ITSM | DevOps | Cloud

Filtering data is at the heart of developer productivity. Whether you’re looking for failed builds, debugging a service or analysing deployment patterns, the ability to quickly slice and dice execution data is critical. At Harness, users across CI, CD and other modules rely on filtering to navigate complex execution data by status, time range, triggers, services and much more.

NoSQL change control must be integrated into CI/CD to ensure governance, traceability, and deployment safety, while automated versioning, testing, and rollbacks reduce compliance risk and preserve release velocity, enabling structured database DevOps to scale innovation without compromising reliability or audit readiness.

For the last decade, the holy grail of DevOps has been Automation. We spent years writing Bash scripts to move files, Terraform to provision servers, and Ansible to configure them. And for a while, it felt like magic. But any seasoned engineer knows the dirty secret of automation: it is brittle. Automation is deterministic. It only does exactly what you tell it to do. It has no brain. It cannot reason.

Q4 2025 delivered major upgrades across Harness Continuous Delivery, GitOps, and Continuous Verification, focused on safer rollouts, stronger infrastructure integrations, and workflows that scale. Here’s a curated roundup of what shipped and where to learn more. Welcome back to the quarterly update series! Catch up on the latest Harness Continuous Delivery innovations and enhancements with this quarter's Q4 2025 release. For full context, check out our previous updates.

We’ve all seen it happen. A DevOps initiative starts with high energy, but two years later, you’re left with a sprawl of "fragile agile" pipelines. Every team has built their own bespoke scripts, security checks are inconsistent (or non-existent), and maintaining the system feels like playing whack-a-mole. This is where the industry is shifting from simple DevOps execution to Platform Engineering.

Backstage alternatives fall into three real choices: build and own a framework, buy a fully managed IDP product, or choose a hybrid path that reduces maintenance but keeps Backstage at the core. The trade-off is not "free vs paid" but engineering headcount, governance maturity, time to value, and how actionable your portal is across CI/CD, IaC, and environments. The best commercial IDPs go beyond catalog and documentation.

Modern application security is struggling to keep up with AI-driven development and cloud-native scale, especially when security feels bolted onto CI/CD instead of built in. Harness SAST and SCA bring AI-powered application security testing natively into the Harness platform, reducing noise and alert fatigue. By identifying only vulnerabilities that are actually reachable in production code, teams get findings they can trust and act on faster.

Harness AI is starting 2026 by doubling down on what it does best: applying intelligent automation to the hardest “after code” problems, incidents, security, and test setup, with three new AI-powered capabilities. These updates continue the same theme as December: move faster, keep control, and let AI handle more of the tedious, error-prone work in your delivery and security pipelines. ‍

Have you ever watched a “temporary” Infrastructure as Code script quietly become mission-critical, undocumented, and owned by someone who left the company two years ago? We can all related to a similar scenario, if not infrastructure-specific, and this is usually the moment teams realise the build vs buy IaC decision was made by accident, not design.

The Argo ceiling is a predictable scaling challenge, not a failure of Argo CD or GitOps. As clusters and teams grow, visibility, governance, and orchestration fragment without a control plane. Script-heavy workflows and manual processes slow delivery and increase risk at scale. A GitOps control plane enables unified visibility, structured workflows, automated guardrails, and secure secret management. GitOps has become the default model for deploying applications on Kubernetes.