AI DevOps & CI/CD
AI DevOps is the practice of adapting CI/CD pipelines, environments, and deployment workflows so AI agents can ship code at machine speed - through governed, auditable paths that coexist with human Git workflows.
Claude Code, Cursor, and Codex turned your engineers into 10x developers. But your CI/CD pipeline - builds, tests, environment provisioning, deployments - was sized for human-speed development. The bottleneck flipped: it is no longer writing code, it is shipping it.
AI DevOps means two deployment paths coexisting on one platform: prompt-to-deploy from any AI agent for speed, and Git-push-to-deploy with Terraform and PR review for production - both policy-gated, both audited.
These guides cover the modern AI-assisted delivery pipeline: ephemeral environments, preview-per-PR, deployment automation, and the path off legacy platforms.
$ curl -fsSL https://skill.qovery.com/install.sh | bash› Deploy my project with QoveryAI DevOps in 2026: How AI Coding Tools Are Breaking Your CI/CD Pipeline (and How to Fix It)
AI coding tools turned every engineer into a 10x developer. Now your CI/CD pipeline is the bottleneck. Learn how to handle 10x more deploys per engineer with Qovery's dual deployment model.
Why There's Never Been a Better Time to Leave Heroku (Especially in the AI Era)
Salesforce just put Heroku into maintenance mode and ended Enterprise sales for new customers. The AI era also flipped the migration math: what used to be a six-month project is now a one-prompt, agent-driven move to your own cloud. Here's why now is the best time to leave Heroku.
Architecting backend preview environments: A guide for DevOps engineers
Stop battling shared staging bottlenecks. Learn how DevOps engineers can architect isolated, ephemeral backend preview environments to accelerate development.
The enterprise guide to DevOps automation: scaling kubernetes and delivery pipelines
Scale your enterprise DevOps automation without configuration sprawl. Learn how a Kubernetes management platform like Qovery enables secure, self-service infrastructure.
Top 10 Internal Developer Platforms That Streamline Software Delivery
Looking for an Internal Developer Platform? Explore the distinct features of top platforms to make an informed choice that best suits your needs.
10 DevOps automation tools to tame Kubernetes in 2026
Compare the top 10 DevOps automation tools for 2026. Discover the architectural strengths and trade-offs of Terraform, ArgoCD, Qovery, and more.
PaaS vs. DIY IDP: The Fastest Path to a Self-Service Cloud
Building an Internal Developer Platform (IDP) from scratch seems cheaper, but the maintenance costs add up. Discover why a modern PaaS on your own infrastructure is the faster, smarter path to a self-service cloud.
What Is an MCP Server for Infrastructure? How AI Agents Deploy Safely
An MCP server is the standardized bridge that lets AI agents like Claude Code and Cursor operate real infrastructure - deploy apps, provision databases, manage environments - through one governed API. Here's how MCP servers work for infrastructure, why they matter, and how to give agents production access without losing control.
The Best AI Coding Agent Sandboxes Compared (2026)
A practical comparison of AI coding agent sandboxes in 2026 - OpenAI Codex, Cursor cloud agents, Claude Code, GitHub Copilot agent, and Qovery. See which give agents real environments to deploy, test, and verify their own code.
The Best Tools for Integrating AI Agents with Kubernetes in 2026
A practical guide to the best tools for both using AI agents to manage Kubernetes (AIOps) and running AI agent workloads on Kubernetes infrastructure in 2026.
How Kubernetes AI Agents Improve Cluster Management
AI agents compress Kubernetes incident diagnosis from 45 minutes to seconds, eliminate YAML authoring toil, and shift resource tuning from static to continuous. Here is what changes concretely when they enter your workflow.
What Is an Agentic Infrastructure Platform - and Why Every Company Needs One
An agentic infrastructure platform is a new category of infrastructure control plane designed for AI agents. It unifies the fragmented toolchain behind one API so agents can operate infrastructure - not just run code - with governance built into every operation. Here's why every company needs one.
3 Platform Engineering Shifts From Devoxx France 2026
Three days, 20 talks at Devoxx France 2026. The through-line wasn't AI hype - it was discipline. Context engineering, code review under AI volume, and the local-vs-remote question now shaping security, cost, and sovereignty.
OpenAI Just Proved Our Thesis: Everyone Is a Builder Now. Here's What Comes Next.
OpenAI reports 5M weekly Codex users - 20% non-developers, growing 3x faster. Six role plugins, hosted Sites, zero enterprise governance. The governed runtime is what's missing.
10 best Red Hat OpenShift alternatives to reduce licensing costs
For years, Red Hat OpenShift has been the safe choice for heavily regulated, on-premise environments. It operates as a secure fortress. But in the public cloud, that fortress acts as an expensive prison. Paying proprietary per-core licensing fees on top of your standard AWS or GCP compute bill is a redundant "middleware tax." Escaping OpenShift requires decoupling your infrastructure from your developer experience by running standard, vanilla Kubernetes paired with an agentic control plane.
What is Kubernetes? The reality of Day-2 enterprise fleet orchestration
Kubernetes focuses on container orchestration, but the reality on the ground is far less forgiving. Provisioning a single cluster is a trivial Day-1 exercise. The true operational nightmare begins on Day 2. Teams that treat multi-cloud fleets like isolated pets inevitably face crushing YAML configuration drift, runaway AWS bills, and severe scaling bottlenecks.
Managing Kubernetes deployment YAML across multi-cloud enterprise fleets
At enterprise scale, managing provider-specific Kubernetes YAML across multiple clouds creates crippling configuration drift and operational toil. By adopting an agentic Kubernetes management platform, infrastructure teams abstract cloud-specific configurations (like ingress controllers and storage classes) into a single, declarative intent that automatically reconciles across 1,000+ clusters.
Everything you need to know about Kubernetes autoscaling at fleet scale
When engineers configure pod autoscaling, they instinctively tie the Horizontal Pod Autoscaler (HPA) to CPU utilization. If the application is actually bound by memory or downstream database connections, the cluster sits idle while incoming requests time out. Diagnosing hundreds of outages reveals a clear pattern: effective elasticity requires scaling on custom application queues, not just default hardware thresholds.
Why is CI/CD the bottleneck for AI-assisted development?
AI agents produce 10 to 20x more deploys per engineer - previews, experiments, prototypes - hitting pipelines sized for human-speed, waterfall development. Queue times grow and agents bypass Git workflows via direct API calls, breaking audit trails. The pipeline, not the coding, becomes the constraint.
How do AI agents and GitOps work together?
On a governed platform, agents deploy to preview environments from a prompt for speed, while production changes go through Git - Terraform, PR review, and merge. Both paths are policy-gated and audited, so velocity and safety coexist instead of competing.
Build with AI. Deploy with Qovery.
Any coding agent builds it; Qovery ships it - governed by construction. Prompt-to-deploy and Git-push-to-deploy on one platform.