Vibe Coding Is Not Enough
Vibe coding accelerates prototypes, but production AI agents need contracts, observability, security guardrails, and accountable engineering to protect revenue.
Table of Contents
Vibe coding works for day zero
If your goal is speed, vibe coding is a gift. You can sketch a product, connect tools, and get something usable in hours. For early validation, that is a superpower.
The trouble is what happens after day zero. Once real users show up, quality stops being a nice-to-have. You need predictable releases, observable failures, and a system your team can reason about when things go wrong at 2:00 AM.
Where it breaks in production
Prototype logic usually hides assumptions that production punishes. Prompt chains drift. Integrations change shape. Latency spikes under traffic. Security checks get skipped in “just one more patch.”
The failure mode is rarely one bad prompt. It is an unowned system with no contract boundaries, weak test coverage on critical paths, and no clear rollback story.
What Karpathy got right
Andrej Karpathy helped mainstream the term “vibe coding,” and he was right to frame it as an exploration mode. Exploration is different from operation. That distinction matters.
Exploration asks, “Can we build this?” Production asks, “Can this keep working, safely, with paying customers?” Same tools, different standard.
The production stack agents need
Production agent systems need discipline in four places:
- Contracts: typed inputs/outputs and explicit tool boundaries.
- Observability: traces, failure reasons, and cost/latency visibility.
- Controls: auth checks, rate limits, and human escalation paths.
- Operations: release gates, rollback plans, and clear on-call ownership.
This is not anti-AI. It is pro-reliability. You can still move quickly, but speed has to compound instead of creating hidden debt.
A better operating model
Keep vibe coding in your process, but constrain it. Use it for ideation, prototypes, and spikes. Then graduate successful experiments into an engineering system with standards.
The teams that win will not be the ones with the wildest prompts. They will be the ones that pair model leverage with boring operational excellence. That is what turns demos into durable businesses.
Internal Links
Keep the context chain going with these related pages.
From Vibe Coding to Agentic
My transition from one-off prompts to orchestrated workflows.
Will AI Agents Replace Developers?
A nuanced view of what changes for engineering teams.
Agentic Development Engagements
How we ship production-grade agent systems with clear outcomes.
10k MRR Execution Experiment
A public build log showing how production discipline compounds AI velocity.
FAQ
Questions after the hot take
Is vibe coding bad?
No. It is one of the fastest ways to test demand. The problem starts when teams treat a prototype workflow as a production workflow.
When should a team move beyond vibe coding?
Move once customers depend on the product: when uptime matters, billing is live, or your team needs repeatable releases with clear ownership.
What changes first when moving to production?
Start with ownership, tests for critical flows, observability, and safer deploy gates. Prompt quality still matters, but system quality matters more.
How do AI agents fit into a production workflow?
Treat AI agents as execution workers inside a governed system: typed contracts, auth checks, retries, human escalation, and measurable SLAs.