Prompt Architect

🏷️ Official Name (Coined Right Here):

🚀 AI Pair Programming or Prompt-First Agent Development (PFAD) using SDD is the New Paradigm

A methodology where developers architect, build, test, and deploy software — especially AI agents — by engineering prompts for AI-powered tools like Cursor and/or GPT-5-Codex, Gemini CLI, and Qwen3-Coder rather than writing code manually.

You are a Prompt Architect.
Cursor and GPT-5-Codex is your AI Compiler.
The Python Interpreter and frameworks like the OpenAI Agents SDK is your Runtime.

Prompt Architect: While "prompt engineer" focuses on crafting effective individual prompts, "Prompt Architect" is an emerging, unofficial title for a role that designs and builds entire prompt-based systems. A prompt architect creates multi-agent workflows, manages context across complex tasks, and designs the overall structure of AI-driven solutions, much like a software architect designs a traditional system. This role is gaining traction in AI-native teams at companies like Anthropic and xAI.

The shift from writing code to engineering prompts for developing powerful AI agents is profoundly transformative.

1. Prompt Engineering for Code Generation

You engineer precise natural language instructions (prompts) to get Cursor (powered by LLMs like Claude 4) and/or GPT-5-Codex to generate, modify, and explain code — without writing it yourself.

🎯 Example:
“Generate an agent with SQLite memory that remembers user’s name.”
→ Cursor writes the Python code.

2. Cursor-First Development

You will use Cursor IDE as your primary interface — treating it not as a text editor, but as an AI pair programmer you command via prompts.

Cursor or GPT-5-Codex isn’t just “VS Code with AI.” It’s a prompt-to-code execution environment.

3. Declarative Programming via Natural Language

Instead of writing imperative code (for, if, def), you declare what you want — and the AI generates how to do it.

This is similar in spirit to:

• Infrastructure-as-Code (e.g., Terraform: “declare what you want”)
• SQL (“what data”, not “how to loop”)
• SwiftUI / Jetpack Compose (“what UI”, not “how to draw pixels”)

But now: “What agent behavior”, not “how to code the class.”

4. Agent-Oriented Prompt Design

You don’t just generate snippets — you design AI agents (via the OpenAI Agents SDK) using layered prompts:

• Personality → via system prompt
• Memory → via SQLite config
• Tools → via tool registration
• Safety → via guardrail prompts

This is meta-engineering: you engineer the engineer (the AI agent) using prompts.

5. Spec-Driven Development (SDD): The Discipline

Definition. SDD builds primarily through detailed specifications (specs) that capture intent, constraints, and acceptance criteria—AI generates the code; engineers guide and decide.

Core loop (“suit + tie”):
This structured workflow contrasts with casual "vibe coding" by emphasizing specs as the source of truth, integrating elements of Test-Driven Development (TDD) for validation while leveraging AI for generation and implementation.

1. Specify via an Architect Prompt (high-level spec focusing on user journeys, experiences, and outcomes; AI generates a detailed specification).
2. Plan with technical details, architecture, stack choices, and constraints (AI generates a comprehensive technical plan).
3. Break into Tasks (small, reviewable, testable units, akin to TDD's isolation; AI decomposes the spec and plan).
4. Implement with AI-generated code, including tests to validate (red-green phases: write failing tests first, then minimal code to pass).
5. Refactor while preserving behavior and passing tests (iterative refinement with checkpoints).
6. Explain with an Explainer Prompt for clarity and documentation.
7. Record and Share via an Architectural Decision Record (ADR) and Pull Request (PR) with Continuous Integration (CI) gates (“no green, no merge”).

Creative momentum is preserved, while quality and traceability are institutionalized.

❌ “Vibe Coding” — Not a Thing

There’s no such concept as “vide coding” in:

• Computer science literature

• AI/ML research

• Software engineering methodologies

• Cursor’s documentation or marketing

• “Vibe coding” — a slang term sometimes used to describe coding based on intuition, flow state, or “just feeling it.” But even that doesn’t apply here — you are being highly intentional with prompt design.

• “No-code” or “Low-code” — but this is prompt-code, which is different: you’re still producing full, deployable, complex code — just not typing it.

• “AI coding” or “LLM-assisted dev” — yes, this fits.

💡 Why This Matters

You’re not replacing coding — you’re elevating it.

Instead of:

def calculate_tax(income):
    return income * 0.2

You’re writing:

“Create a tax agent that calculates federal tax using 2024 US brackets, remembers user’s last income, and explains the calculation in plain English.”

The AI writes the 200-line implementation.
You designed the behavior, constraints, memory, and UX — in one sentence.

That’s higher-order engineering.

✅ Summary

Go forth and build agents — not with code, but with intent, precision, and language.

The compiler is listening. 🎧🤖