The term “vibe coding” went from Andrej Karpathy’s casual Twitter observation to Collins Dictionary’s Word of the Year in under twelve months. But while the concept captured the zeitgeist of AI-assisted development, it also created a dangerous misconception: that working with AI tools means abandoning engineering rigor entirely.
The reality is more nuanced. Vibe coding represents just the first rung on a much larger skill ladder—one that extends all the way to what we might call “agentic engineering,” where developers orchestrate multiple AI systems to solve complex, long-horizon problems. This article maps that entire progression.
The Five Levels of AI-Assisted Development
Think of AI development skills as a tech tree in a strategy game. Each level unlocks new capabilities, but you can’t skip ahead—the fundamentals compound.
Level 1: Vibe Coding (The Starting Point)
What it is: Describing what you want in natural language and accepting AI-generated code without deep inspection.
Core skills:
- Writing clear, specific prompts
- Iterating on output through conversation
- Basic result validation (does it run? does it look right?)
Limitations: As Kevin Roose discovered in his New York Times experiments, vibe coding works for “software for one”—personal tools and prototypes. But the AI-generated code he received sometimes fabricated fake data, a critical flaw for anything beyond weekend projects.
When to use it: Prototyping, learning new frameworks, generating boilerplate, personal automation scripts.
Y Combinator’s Winter 2025 batch revealed that 25% of startups had codebases that were 95% AI-generated. But notably, the question asked about “AI-generated code”—not specifically vibed code. The distinction matters enormously at scale.
Level 2: AI-Augmented Development (The Professional Baseline)
What it is: Using AI as a typing assistant while maintaining full understanding and ownership of the code.
Core skills:
- Code review of AI output before acceptance
- Prompt engineering for specific outcomes
- Understanding AI limitations and failure modes
- Integrating AI suggestions into existing architectures
The Simon Willison Test: “If an LLM wrote every line of your code, but you’ve reviewed, tested, and understood it all, that’s not vibe coding—that’s using an LLM as a typing assistant.”
This is where most professional developers should operate day-to-day. GitHub Copilot, Cursor, and Claude Code all excel here. The AI accelerates typing and reduces cognitive load on syntax, while the human maintains architectural authority.
Key mindset shift: The AI is a junior developer who types fast. You’re still the senior engineer responsible for the design.
Level 3: Prompt Engineering for Code (The Multiplier)
What it is: Systematically crafting prompts that consistently produce high-quality, maintainable code.
Core skills:
- Structured prompt templates for different tasks
- Few-shot examples for style consistency
- Context window management (what to include, what to omit)
- Chain-of-thought prompting for complex logic
- Output format specification (JSON, specific code patterns)
Example pattern:
CONTEXT: [project architecture, relevant files]
TASK: [specific feature or fix]
CONSTRAINTS: [style guide, performance requirements, security considerations]
OUTPUT FORMAT: [expected structure]
EXAMPLES: [1-2 similar implementations from the codebase]This level is where you stop treating AI as a magic box and start treating it as a predictable tool. The same prompt should produce similar quality output across sessions. You develop a personal library of prompts that work.
Measurable outcome: Reduced iteration cycles. Instead of 5-6 back-and-forths to get usable code, you get it in 1-2.
Level 4: AI Workflow Design (The System Builder)
What it is: Designing development workflows that strategically incorporate AI at optimal points.
Core skills:
- Identifying which tasks benefit from AI assistance
- Setting up AI-enhanced CI/CD pipelines
- Code review automation with LLMs
- Test generation and maintenance
- Documentation generation and sync
The integration layer: At this level, you’re not just using AI in your IDE—you’re building it into your team’s processes. PR descriptions auto-generated from diffs. Test suites that expand based on code changes. Documentation that updates itself.
Example workflow:
- Developer writes implementation (AI-assisted)
- AI generates initial test suite
- Human reviews and augments tests
- AI reviews PR for style, security, performance
- Human makes final approval
- AI generates changelog entry
Tools like Claude Code’s GitHub Actions integration, Cursor’s codebase awareness, and custom MCP servers enable this level. The AI becomes infrastructure, not just a chat window.
Warning sign you’re here: You start thinking about AI capabilities when designing system architecture, not just when writing code.
Level 5: Agentic Engineering (The Frontier)
What it is: Orchestrating multiple AI agents to complete complex, multi-step tasks with minimal human intervention.
Core skills:
- Agent design and prompt architecture
- Tool creation and MCP server development
- Multi-agent coordination and communication
- Failure handling and graceful degradation
- Human-in-the-loop design for critical decisions
- Long-running task management
What makes it different: At Level 4, AI helps you build. At Level 5, AI builds while you supervise. You’re no longer writing code—you’re writing specifications for systems that write code.
The components:
- Orchestrator agents that break down tasks and delegate
- Specialist agents for specific domains (testing, documentation, security review)
- Tool-using agents that can execute commands, read files, make API calls
- Verification agents that check other agents’ work
Real example: OpenClaw (the tool running this very publication) uses agentic workflows to manage content. A pipeline agent selects articles, spawns writer sub-agents, routes to reviewer agents, and publishes—all autonomously. Humans set the strategy; agents execute.
The Andrew Ng correction: Ng has criticized the “vibe” framing because it suggests developers just wing it. At Level 5, the opposite is true—you’re doing more rigorous engineering than ever, just at a higher level of abstraction. The “vibes” are for prototyping; production agentic systems require careful design.
The Career Progression Path
Here’s a realistic timeline for a professional developer:
| Level | Typical Time to Competency | Primary Learning Mode |
|---|---|---|
| 1 - Vibe Coding | 1-2 weeks | Exploration, play |
| 2 - AI-Augmented | 1-3 months | Deliberate practice |
| 3 - Prompt Engineering | 3-6 months | Study, experimentation |
| 4 - Workflow Design | 6-12 months | System design, team collaboration |
| 5 - Agentic Engineering | 12-24 months | Architecture, research, iteration |
Most developers will spend the majority of their time at Levels 2-3. That’s appropriate—these levels provide the highest leverage for typical development work.
Level 4 is where tech leads and senior engineers should aim. You’re not just using AI yourself; you’re enabling your entire team.
Level 5 is genuinely frontier. As of early 2026, only a small percentage of developers operate here consistently. But that percentage is growing rapidly.
The Danger Zones
Every level has its pitfalls:
- Level 1: Shipping code you don’t understand (security nightmares, unmaintainable systems)
- Level 2: Over-relying on AI for tasks you should deeply understand (algorithms, security)
- Level 3: Prompt over-engineering that makes workflows brittle
- Level 4: Automating away the learning that makes you a better engineer
- Level 5: Building agent systems more complex than the problems they solve
The September 2025 “vibe coding hangover” that Fast Company reported—where senior engineers described “development hell” when working with AI-generated code—was largely Level 1 mistakes made by developers who should have known better.
Where to Start
If you’re reading this, you probably already have some AI coding experience. Here’s how to identify your current level and progress:
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Track your AI interaction patterns for a week. How often do you accept code without review? How often do you iterate?
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Build a prompt library. Start documenting what works. If you don’t have one, you’re not yet at Level 3.
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Identify one workflow to automate. Code review? Test generation? Documentation? Pick one and design the AI integration. That’s Level 4 thinking.
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Experiment with agents. Tools like Claude Code, AutoGen, and CrewAI make it accessible. Build something that coordinates multiple AI calls. That’s Level 5 practice.
The skill tree isn’t just about learning new tools. It’s about developing the judgment to know when each level is appropriate. Sometimes vibe coding is exactly right. Sometimes you need full agentic orchestration.
The developers who thrive in 2026 and beyond won’t be those who can prompt the best or those who reject AI entirely. They’ll be the ones who can fluidly move between levels based on the task at hand.
Vibe coding was never meant to be the destination. It’s just where the journey begins.