Vibe coding refers to the practice of using natural language prompts to generate code, enabling anyone to move from idea to implementation with unprecedented speed.

While the promise of faster prototyping and shorter production cycles is compelling, the potential risks and drawbacks are equally significant. Trust, transparency, and control become harder to guarantee when humans no longer write or fully review code. Ensuring code is robust, reliable, scalable, and secure enough for production is another important consideration.

These concerns raise a critical question: can vibe-coding be trusted for full-scale software production?

From prototyping to production

Prototyping requires quick action to determine whether an idea is valuable. Whereas production involves a series of steps that include testing, infrastructure management, safe deployment strategy, monitoring, and more. Though AI can accelerate some steps, production ultimately depends on people and processes, not just technology.

Vibe coding often falls short in production due to AI’s lack of contextual understanding that human developers bring to their work. In situations where a human would instinctively avoid risk, like deleting production databases or exposing secret information, AI tools, in contrast, may act unpredictably. Such incidents highlight the critical need for oversight and guardrails when working with autonomous systems. Without them, even well-intentioned AI systems can introduce serious vulnerabilities.

So we ask the questions, is there a difference between testing human-generated code and AI- generated code?

When AI rewrites itself

Software development has long relied on static codebases where code is written, tested, and subsequently deployed, remaining unchanged unless a developer manually updates and redeploys it. Now, some vibe coding tools are introducing dynamic codebases capable of autonomously rewriting and redeploying code based on new inputs or evolving interpretations of their goals. This shift challenges foundational practices like peer review and user acceptance testing.

If the code can change itself, who reviews it and when? Can we trust automated systems that write and rewrite their own code to validate their own logic, especially as changes occur outside of human oversight?

We’ve previously explored the importance of purpose and intent in autonomous systems, as well as how these shape emergent behavior. When a codebase becomes dynamic, it’s the AI’s interpretation of the system’s purpose and intent that drives decision-making. Resulting behaviors may evolve with time, and the trajectory of their evolution depends entirely on how well the original intent was defined.

Do we need more robust testing to not only test what the code can do today but also what it might be capable of being reprogrammed to do in the future?

Building confidence in autonomous and agentic systems

These questions around trust, oversight, and responsible evolution aren’t just unique to vibe coding, they’re central to a broader conversation about autonomous and agentic systems. As AI tools begin to operate with more independence, the challenge shifts from simply verifying outputs to understanding how and with what intent decisions are made.

Our whitepaper, Confidence in autonomous and agentic systems, outlines several critical dimensions for operating multi-agent systems and testing in this new landscape, including:

  • Desired emergent behavior
  • Purpose and intent clarity
  • Effective communication protocols
  • Humility in decision-making models

Throughout the white paper, we explore the transformative potential of autonomous and agentic systems, as well as the new challenges they present. Autonomous systems, of which many are being made using vibe coding, present significant opportunities across industries. But without the right technical, ethical, and organizational considerations in place, businesses can fail to successfully access these opportunities, or worse, cause true harm to their solutions, infrastructure, and customers.

Evolving with intent

While vibe coding does offer undeniable advantages in terms of prototyping, speed isn’t all that software developers should keep in mind. When it comes to production, the stakes are much higher. Code must be robust, secure, and aligned with long-term intent. Without clear oversight and thoughtful integration, dynamic code bases risk introducing instability into systems that demand reliability.

Are we pushing the user acceptance testing of emergent behaviors from AI systems onto the users themselves in production?

The dimensions outlined in Confidence in autonomous and agentic systems provide developers with a framework for evaluating whether vibe coding tools are ready for more than just prototyping. Trust in production demands alignment, oversight, and a holistic understanding of a given system, and our whitepaper provides you with a framework for getting there. Though vibe coding may be ready for prototyping, whether it’s ready for production depends entirely on how well we prepare the systems, and ourselves, for what will come in the future.

AI Futures Lab We are the AI Futures Lab – expert partners that help you confidently visualize and pursue a better, sustainable, and trusted AI-enabled future. We do this by understanding, preempting, and harnessing emerging trends and technologies. Ultimately, making possible trustworthy and reliable AI that triggers your imagination, enhances your productivity, and increases your efficiency. We will support you with the business challenges you know about and the emerging ones you will need to know to succeed in the future. Build your AI advantage, layer by layer. Backed by extensive research and collaboration, we’re best placed to help you navigate the AI landscape and establish AI solutions that herald a step change in how we can solve business problems, holistically. Engage with us – let us surprise you with our visionary mix of what’s to come.