Why "Vibe Coding" AI Apps Fail in Production: The Missing Ecosystem Problem

Why "Vibe Coding" AI Apps Fail in Production: The Missing Ecosystem Problem

Most AI applications today are built through "vibe coding" - endlessly tweaking prompts until a demo works perfectly. But this approach crumbles the moment real users interact with the system, exposing a fundamental misunderstanding about what production AI actually requires.

The problem isn't talent or ambition. It's that we're treating AI applications as one-liners instead of systems. We're trying to vibe our way into production while skipping the entire engineering discipline that makes traditional software reliable.

The Reality Check: Production AI Needs More, Not Less

Building reliable AI apps requires even more infrastructure than traditional engineering, not less. Here's what most teams miss:

Evaluation Systems: You need robust evals to understand behavior across thousands of edge cases, not just the happy path tested manually. When traditional apps break, you get stack traces. When LLMs misbehave, you get subtle wrongness that slides past until customers complain.

Continuous Optimization: LLMs drift, contexts shift, and prompts decay. What worked last month stops working because models update or user behavior changes. You need systems that detect degradation and improve automatically, not founders frantically rewriting prompts at 2 AM.

Memory and State Management: Real applications maintain continuity across sessions. Most vibe-coded apps forget everything between requests, expecting users to re-explain their situation every time.

AI-Specific Observability: You need to know when your AI is uncertain, making things up, or degrading gracefully versus failing catastrophically. Traditional logging isn't enough for hallucination detection.

Why Demos Shine But Production Burns

The demo environment is controlled with curated examples and perfect prompts tweaked for specific cases. Everything works because the environment is constrained and test cases are known.

Then real users arrive with messy data. The perfect prompt suddenly fails 30% of the time, and nobody knows why because there's no evaluation framework to measure it. The magic disappears when the guardrails do.

Building the Missing Engineering Discipline

If you want AI features with code-like reliability, give them what code has always had: structure, tooling, guardrails, and continuous improvement. This means:

• Version control for prompts and evaluations
• Testing frameworks that run automatically
• Monitoring that alerts when behavior degrades
• Rollback capabilities when deployments fail
• Team workflows for safe multi-contributor development
• Environment separation for dev, staging, and production

The companies building reliable AI features understand this isn't theoretical - it's basic software engineering applied to a new domain. Security, compliance, reliability, and quality don't disappear because it's new technology.

Platform Thinking vs. Prompt Thinking

Real AI applications need platform thinking, not prompt thinking. They need specialties, tools, and discipline just like every other branch of engineering. The question for every founder building with AI is simple: Are you building a demo or a system?

Because if you want your AI feature in production, it's time to stop vibe coding and start engineering.

🔗 Read the full article on The New Stack