My journey to automatically update a website from a Telegram chat.
In today’s AI gold rush, speed is a commodity. AI can clone sites in minutes and generate code in seconds. But in production, speed without governance is a liability. Imagine one prompt triggering an unintended deploy to prod.
To address this, I have codified a rigorous operational standard: The Adrian Method of AI-Driven CMS Governance (GitHub). The SOP and diagram are registered under my name in Indonesia (DJKI No. 001149429, Feb 2026). Anyone can reuse and adapt it under CC BY 4.0—just keep attribution.
The Governance Crisis in AI Automation
Most AI implementations focus on capability (what the AI can do) but ignore accountability (who is responsible when it fails). Traditional CMS architectures are not designed for the unpredictable nature of Large Language Models (LLMs). A hallucinated feature, a wrong price, or a broken layout can become a public incident.
My Core Aspirations for AI Governance
Before diving into the technical phases, I want to clarify the two pillars that drive this framework:
AI Safety through Accountability (HITL): I believe that AI safety is not just a theoretical concept; it is an operational requirement. By implementing a Human-in-the-Loop (HITL) protocol, we ensure that every AI-generated product is auditable and accountable. The final decision remains a human responsibility, ensuring the output aligns with ethical and professional standards.
Augmentation, Not Replacement: This framework is designed to empower engineers to do more, not to replace professional programmers. Human ingenuity—the ability to design complex architectures and navigate nuanced logic—is irreplaceable. This method is a tool to free developers from repetitive tasks, allowing them to focus on high-level innovation.
The Solution: The 4-Phase Protocol
The Adrian Method is a non-bypassable workflow designed to ensure AI remains a secure asset.
Phase I: Intent Ingestion & Normalization
Natural language is inherently ambiguous. This phase ensures that user intent is parsed and normalized into a structured technical schema. If the intent is unclear, the system is hard-coded to halt and request clarification.
Phase II: Isolated Staging (Shadow Environment)
AI-generated assets are never allowed to touch the production environment directly. They are rendered into a "Shadow Environment"—an isolated, unindexed static deployment where the AI’s output is visualized but contained.
Phase III: The Human-in-the-Loop Gate (The Firewall)
This is the mandatory pause. Through a secure Command & Control interface (implemented via Telegram Bot API), a human stakeholder must inspect the Shadow Environment. Human judgment is the final barrier against AI hallucinations.
Phase IV: Atomic Promotion
Only after explicit human authorization is the build promoted. We utilize Atomic Swaps on Static Blob Storage to ensure zero-downtime and instantaneous global consistency.
The Operational Flow
[User Prompt]
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v
PHASE I: Intent Ingestion
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v
PHASE II: Isolated Staging
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v
PHASE III: Human Gate ---- Rejected ---> [Abort / Revise]
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Approved
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PHASE IV: Atomic Promotion
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[Live & Audited]
Real-World Case Study: Ksatriamitra.com
We applied this framework to Ksatriamitra.com, an IT service provider. By shifting to a Telegram-Driven CMS backed by The Adrian Method:
Operational Security: The owner manages site updates via mobile chat, but no change goes live without an "Approve" click.
Infrastructure Resilience: The site is hosted as immutable static files, making it inherently resistant to server-side attacks.
Cost Optimization: By utilizing GitHub Actions for on-demand rendering and Blob Storage, the "idle cost" is effectively zero.
Why Governance is the New Standard?
As engineering leaders, our responsibility is moving beyond code because we are now architects of autonomous systems. If you are experimenting with AI-driven automation, I suggest, don’t start with “how do we deploy faster?” but start with “how do we prevent a bad deploy?”
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