{
  "id": 15037280,
  "title": "From Vibe-Coded to Trusted: One Agent Through Build, Scale, Govern and Optimize on Gemini Enterprise Agent Platform",
  "url": "https://urgent.news/2026/10/16/from-vibe-coded-to-trusted-one-agent-through-build-scale-govern-and",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-16T20:34:07.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/gde/from-vibe-coded-to-trusted-one-agent-through-build-scale-govern-and-optimize-on-gemini-4np1"
  },
  "original_language": "en",
  "account": "The Gemini Enterprise Agent Platform offers a structured approach to building, scaling, governing, and optimizing agents. By following the Build, Scale, Govern, and Optimize framework, one can create agents that perform specific tasks while ensuring reliability, security, and continuous improvement.\n\nIn the featured example, an ambient expense approver agent was developed using Google's codelab Vibecode as the starting point. The agent follows a set of rules: under $100, the approval is automatic; for amounts of $100 or more, a security checkpoint runs, and Gemini reviews the risk before allowing a human to make a decision. This approach ensures that simple cases are handled automatically, while more complex or risky cases are handled by humans.\n\nThe article highlights key considerations throughout the development process, such as ensuring proper security measures, avoiding prompt injection attacks, and providing clear explanations of how the approval decision works. By using Antigravity, Google's agentic IDE, and the Agents CLI, the development process was streamlined, allowing for quick creation and review of the agent's code.\n\nThe article also emphasizes the importance of testing and validation. 33 unit tests were written to cover various scenarios, including the DISAPPROVE case and social security number formats. These tests help ensure the agent's functionality and catch any potential bugs early in the development process.\n\nOnce the agent is built and tested, it can be deployed using the Agent Runtime, a managed runtime that hosts and autoscales the agent. This ensures that the agent can handle a large number of requests without manual intervention, making it highly scalable. Additionally, every deploy is tracked using traces, which show what actually ran during each execution, allowing for further analysis and debugging if necessary.",
  "summary": "Building an agent is easy now. Trusting it is the hard part. Here's how I took one agent all the way, with real screenshots and the code on GitHub. Building an agent is easy now. With a coding agent and one of Google's codelabs, I had a working one in about a day. Trusting it is the hard part. When you show an agent to a security team or a business owner, nobody asks \"does it run?\" They ask four…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}