In this post, I’ll walk you through the workflow I use to move from idea to production:

• Structured feature definition
• UI exploration in v0
• Integration in Cursor
• Human review and refactoring
• What didn’t work (and how I adapted)

This isn’t theory. It’s the system I use to ship real features.

The Shift: From Vibe to Structure

Vibe coding looks like this:

“Build a workout overview page with filters and charts.”

Prompt.

Copy.

Adjust.

Repeat.

It works surprisingly well — until it doesn’t.

The problems show up when:

• The feature must live long-term
• Architecture matters
• Responsive behavior gets complex
• Multiple engineers touch the code

Agentic engineering adds discipline:

• Clear goals before generation
• Tool separation
• Controlled integration
• Mandatory human review

You stop prompting randomly.

You start orchestrating.

The Workflow

Step 1 — Structured User Story

Everything starts with clarity.

Before opening v0 or Cursor, I generate a structured feature definition using ChatGPT or Gemini.

I want:

• Context
• User persona
• Detailed functional requirements
• Non-functional constraints
• Edge cases
• Acceptance criteria

This dramatically improves output quality downstream.

If you’ve read my model benchmark, you’ve seen how reasoning quality differs across models. Structured input amplifies those differences.

👉 Related: Latest LLMs in the Test: GPT 5.1 Codex Max vs. Gemini Pro 3 vs. Opus 4.5

Full Structured User Story Prompt Template

You can copy this directly:

I want to define a new feature for my application.

Please generate a structured user story with:

1. Context
2. User Persona
3. Problem Statement
4. Goal / Desired Outcome
5. Functional Requirements
6. Non-Functional Requirements
7. Edge Cases
8. Data & State Requirements
9. Acceptance Criteria
10. Suggested Component Structure (no implementation code)

Format in Markdown with clear headings.
Be specific and avoid generic phrasing.

This template forces depth before generation.

That’s where agentic engineering begins.

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Lightweight Version (For Faster Iteration)

For internal tools or experiments, I use this:

Generate a concise structured user story including:

1. Goal
2. Target User
3. Core Requirements
4. Constraints
5. Edge Cases
6. Acceptance Criteria

Keep it short and specific.

Use lightweight for exploration.

Use full for production.

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Step 2 — UI Exploration in v0

Once the feature is defined, I move to v0.

Here I:

• Paste the structured user story
• Add branding screenshots
• Constrain layout
• Iterate until the hierarchy feels right

This is where vibe coding still shines — rapid iteration.

v0 can also generate implementation code and turn it into a pull request.

I recommend using that PR as the starting point, then iterating and hardening the result in Cursor.

Step 3 — Integration in Cursor

I never paste raw v0 output into production.

Instead, I treat it as design intent.

In Cursor:

• I build on top of the existing project
• I provide screenshots from the v0 design
• Use the existing PR from v0
• I provide clear constraints

I usually start in Plan mode so Cursor can figure out the best implementation path first. I iterate on the plan until it fit's what I want.

From there, I build on top of the Pull Request generated by v0. In Cursor Plan mode, I re-verify the generated logic against existing types, conventions, and architecture.

This is a multimodal workflow: I give the model text plus screenshots, so it can reason with visual context instead of text alone.

Example integration instruction:

Goal: Implement this feature in the existing project.

Context:
- Existing codebase and architecture patterns are the source of truth.
- I attached screenshots as visual reference for layout, hierarchy, and UX details.
- Build on top of the code changes already present in this branch.

Requirements:
- Follow existing architecture and conventions.
- Reuse existing components/utilities where possible.
- Avoid introducing unnecessary dependencies.
- Refactor only where it clearly improves maintainability.
- Add or update tests for changed behavior.

Deliverables:
1. Short implementation plan.
2. Production-ready code changes.
3. Brief explanation of architectural decisions and trade-offs.

This step transforms generated UI into maintainable software.

If you’ve read my frontend generator comparison, you know different tools produce very different structural quality.

👉 Related: AI Frontend Generator Comparison: Claude Code vs v0 vs Cursor vs Replit

Integration is where those differences really show.

Where I can still improve this workflow: I can make this process more repeatable by codifying standards in .cursorrules and creating a dedicated Cursor Skill for this implementation pattern. This is something I need to research further in the future.

Step 4 — Architectural Gatekeeping

Garbage in, garbage out.

If the inputs are vague, the implementation quality degrades fast.

So Architectural Gatekeeping is non-negotiable.

AI output often:

• Over-abstracts
• Adds unnecessary complexity
• Misses subtle edge cases
• Optimizes locally, not system-wide

So I:

• Simplify logic
• Remove redundant abstractions
• Align naming conventions
• Optimize state handling
• Adjust performance-sensitive areas

Some checks:

• Abstraction check: Did the AI create a new component for something that already exists in our library?
• State ownership: Is state living in the right place (local vs. global)?
• Error handling: Did it implement real try/catch handling, or leave placeholders like // TODO: handle errors?
• Prop flow: Did it introduce avoidable prop-drilling where shared state or composition would be cleaner?
• Utility duplication: Did it create new helper functions that duplicate existing utilities in the codebase?

That’s normal.

AI accelerates.

Humans decide.

Step 5 — Test, Merge, Ship

Run Cursor Review mode to surface potential integration errors.

Before merging:

• Tests pass
• New tests added where needed
• No unnecessary dependencies
• Performance verified
• Feature flags if required

Why This Workflow Scales

This works because:

• Design and integration are separated
• AI is used intentionally
• Review is mandatory
• The process is repeatable

You can give this workflow to another engineer and expect similar quality.

That’s when AI becomes leverage — not chaos.

When I Still Vibe

Vibe coding still has a place.

I use it for:

• Experiments
• Internal tools
• Landing pages
• Rapid UI exploration

The key difference is awareness.

If I’m vibing, I know I’m vibing.

If I’m shipping, I switch to the agentic engineering workflow.

Workflow Summary

Phase	Tool	Key Output	Human Role
Definition	ChatGPT / Gemini	Structured User Story	Architect: Defining boundaries
Exploration	v0	UI/UX and Initial PR	Designer: Curating the "Vibe"
Integration	Cursor	Hardened Code	Engineer: Enforcing patterns
Validation	Human / Review	Production-Ready	Gatekeeper: Final accountability

Final Thoughts

AI didn’t remove engineering discipline.

It made it more important.

Vibe coding unlocked creativity.

Agentic Engineering unlocks reliability.

Vibe coding is a loan you take out against your future self; agentic engineering is the down payment on a maintainable codebase.

The future belongs to engineers who can orchestrate intelligent systems — not just prompt them.

And that’s a fundamentally different skill.