🎯 Vibe Coding with AI Agents: What Actually Works

When it comes to coding with AI agents, most developers fall into one of two traps:

1. Dump everything — a wall of requirements in a single prompt.
2. Start coding cold — hoping the agent "just gets it."

Both lead to what we call: spaghetti output.

Here’s what actually works in practice when you're coding with AI.

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🧩 Start with Interface Design

“Tell the agent what classes to care about, not just what problem to solve.”

Instead of explaining the entire use case upfront, write high-level class or module definitions first. This acts as a skeleton and gives structure to the agent's reasoning.

✅ Good prompt:

class QueryPlanner {
  plan(): Plan[]
}

Follow up with: "Now implement this based on user input…"

Why it works: LLMs are great at filling gaps, but not great at building the frame.

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🔬 Scope It Tighter

Vibe coding works best when you constrain the context window.

"Give it less, guide it more."

Instead of a big problem blob, chunk your work:

• Break large tasks into subproblems
• Prompt the agent on each chunk with a clear goal
• Use role-based prompting: "You are a planner… now you're an executor."

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✅ Write Tests First

Yes, even for AI.

Giving agents tests first creates a performance boundary. It tells them what “done” looks like.

// Goal: write a planner that outputs valid steps
expect(plan).toContain('search Amazon')

Agents that know the output constraints write cleaner, more relevant code.

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✍️ Prompt Like a Designer

• Avoid long prose.
• Use code blocks, short bullets, and examples.
• Use consistent naming: "agent", "task", "goal".
• Be specific in stages: e.g., “Now plan”, “Now execute”, “Now test”.

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🧠 Final Word

Vibe coding isn’t just vibes.
It’s interface-first, scoped prompting, and test-driven generation.

And when it clicks, it feels like pair programming with a genius assistant.

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Originally published on AgentNet