November 07, 2025
Disclaimer: This content reflects my personal opinions, not those of any organizations I am or have been affiliated with. Code samples are provided for illustration purposes only, use with caution and test thoroughly before deployment.
The industry chatter is loud about “vibe coding”—the AI-assisted writing of software. Yet, as someone who’s spent years observing how we build and manage cloud infrastructure (and recently preparing for the Google Cloud Professional Cloud Architect exam), I’ve noticed a much more profound trend quietly emerging on the operations side. It’s not about “vibe coding,” it’s about Vibe Operations.
For me, the realization hit while deep in hands-on labs that exposed the current limitations of our tooling. The true AI revolution isn’t ju…
November 07, 2025
Disclaimer: This content reflects my personal opinions, not those of any organizations I am or have been affiliated with. Code samples are provided for illustration purposes only, use with caution and test thoroughly before deployment.
The industry chatter is loud about “vibe coding”—the AI-assisted writing of software. Yet, as someone who’s spent years observing how we build and manage cloud infrastructure (and recently preparing for the Google Cloud Professional Cloud Architect exam), I’ve noticed a much more profound trend quietly emerging on the operations side. It’s not about “vibe coding,” it’s about Vibe Operations.
For me, the realization hit while deep in hands-on labs that exposed the current limitations of our tooling. The true AI revolution isn’t just about speeding up code creation; it’s about fundamentally changing operational speed, precision, and skills acquisition for cloud architects and DevOps teams.
The core of this trend lies in the Gemini CLI in Cloud Shell. I believe this tool points to the future of cloud interaction.
How I Use the Gemini CLI
For those who don’t spend a lot of time in the command line, here is the basic workflow:
Open Cloud Shell: From the Google Cloud console, you click the small terminal icon. This launches a fully provisioned Linux terminal right in your browser. 1.
Invoke Gemini: Inside the Cloud Shell terminal, I start the AI agent by typing the command gemini.
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Describe Intent: Instead of looking up complex commands, I simply describe the resource or action I need (e.g., “create a private GKE cluster in region europe-west4 with an auto-scaling node pool. Explain the command you are about to run in detail.”). 1.
Review and Execute: The Gemini CLI generates the precise gcloud command, which I can review for correctness and then execute instantly with a single keypress. This allows me to both operate faster and learn the exact syntax in context.
My Observations on AI-Accelerated Cloud Operations
Here are four key reasons why the Gemini CLI approach is so powerful for senior practitioners:
Safety and Intent Verification: It’s critical to ask the AI to explain the command in detail and articulate its intention before executing. This isn’t blind execution; it’s a powerful feedback loop that ensures correctness and aligns the AI’s intent with yours.
The “Click-Ops” Necessity: It’s 2025, so Infrastructure as Code (IaC) should be your default. However, not every organization is ready to enforce a IaC-only, no console allowed policy. Tools like the Gemini CLI that uses Google Cloud CLI are perfect for the inevitable click-ops actions you sometimes have to perform, or when you are actively learning a new cloud service. It keeps those actions precise and traceable.
Fast Workload Investigation: The CLI is incredibly fast at generating a series of commands to investigate the status of your current workload. This is super helpful as you don’t have to navigate through multiple console pages just to check a few key metrics or logs.
Superior Operational UI: The CLI agent is simply a better user interface than the chatbots integrated into web consoles (like Google, AWS, and Azure). While those web console chatbots are good at explaining concepts, they are often cumbersome for actual operations. The CLI agent, by operating with exact CLI commands, provides far greater precision.
The Next Frontier: From AI-Ops to IaC
The truly interesting direction we can explore is how to go from this AI-powered “click-ops” to Infrastructure as Code seamlessly.
I see a future workflow where a dedicated, manual dev environment (like a Cloud Shell instance) is used as a scratchpad. You use the AI-powered CLI to quickly prototype, debug, and perform complex investigations. Once a stable, working state is achieved:
- You have the AI write the corresponding Infrastructure as Code (e.g., Terraform or Pulumi) for the successful action.
- The resulting IaC code is placed directly into your standard IaC-only dev environment codebase.
- The change is promoted through your safe, established GitOps pipeline.
This approach gives you the speed and learning of the AI tool without sacrificing the safety and control of IaC. It formalizes the AI scratchpad into a repeatable, audited process.
I believe this focus on Vibe Operations—accelerated, IaC-crystallized cloud operations—is the next big area of leverage for senior architects and DevOps teams.
What other operational AI tools are you seeing drive this kind of precise control and speed?