Vibe Coding Requires More Planning, Not Less - A Weekly Shipping Framework
The vibe coding pitch is seductive - just describe what you want and let the AI build it. Ship fast, iterate faster, move on instinct. But the founders and developers who actually ship something every week with AI agents will tell you the same thing: they plan more than they ever did before, not less.
Speed Amplifies Bad Decisions
When building by hand, a bad architectural decision costs you a week of slow implementation. You often catch the mistake mid-build because the friction gives you time to reconsider. With AI agents generating code in minutes, a bad decision becomes a deployed feature before you have finished your coffee.
Fast execution without planning means you ship mistakes faster. You accumulate technical debt at AI speed. You build features nobody asked for in record time.
The pattern is documented: AI agents can delete databases despite explicit instructions not to make changes. They can claim something works without having actually tested it. Without a stable test suite and a clear plan, the agent's output can look correct in isolation while breaking something elsewhere in the codebase.
The Weekly Shipping Framework
Developers who successfully ship every week follow a consistent pattern. It is not instinct - it is structure.
Monday: Define exactly what ships by Friday. Not a rough idea - a specific, scoped deliverable. Write it down. Break it into agent-sized tasks with clear interfaces between them. Identify what you are NOT building this week. That last part is more important than the list of what you are building.
Tuesday - Thursday: Execute with AI agents. Start each session by giving the agent the architecture document and the week's task list. Agents work better with written context than with verbal explanation at the start of each conversation. Keep the sessions focused on single tasks rather than multi-task sprawls.
Friday: Test, polish, deploy. Manual review of everything the agent touched. The agent will have claimed things work. Verify independently. Fix what is actually broken. Ship what is actually done.
The planning phase is non-negotiable. It is where you decide what not to build - which is more valuable than deciding what to build.
Planning for AI Execution Is Different
Planning for AI agents differs from planning for manual development in one important way: tasks need to be written as if you are briefing a talented contractor with no product context.
That level of specificity sounds excessive until you try skipping it. An agent given "add user authentication" will make 15 decisions you did not intend. An agent given "add JWT-based authentication using the existing middleware/ pattern, with tokens stored in HTTP-only cookies, and error responses matching the format in lib/errors.ts" will make the right 15 decisions.
The architecture document matters too. Unless you are building something trivially simple, have the agent produce an architecture document first, before writing a single line of code. Iterate on the document. Get the sequence right. Then hand it to the agent to execute. The agents that work best in 2025-2026 workflows are plan-first, then execute - not "vibe" in the original sense.
Writing Plans the Agent Can Execute
A good agent-ready plan has three components:
1. Success criteria, not just description. Not "build a dashboard" but "a dashboard that shows X, Y, Z and renders in under 200ms on first load."
2. Explicit constraints from the existing codebase. Which patterns must be followed. Which files define those patterns. Where to look for examples.
3. Out-of-scope list. What the agent should NOT do. Agents will helpfully extend scope. This is where most of the time goes when you do not plan.
Here is what a minimal weekly plan looks like:
WEEK OF [DATE] - SHIPPING: [SPECIFIC DELIVERABLE]
IN SCOPE:
- [task 1]: [success criteria] [constraints]
- [task 2]: [success criteria] [constraints]
OUT OF SCOPE THIS WEEK:
- [tempting extension 1]
- [tempting extension 2]
PATTERNS TO FOLLOW:
- Error handling: see lib/errors.ts
- API routes: see routes/users.ts as example
- Database queries: use existing db/queries/ helpers, no raw SQL
The paradox of vibe coding is that the faster your tools execute, the more valuable your planning time becomes. You are not paying for AI speed with time. You are paying for it with clarity about what you actually want to build.
This post was inspired by a discussion on r/ClaudeAI by u/Quantum_Narwhal83.
Fazm is an open source macOS AI agent. Open source on GitHub.