Posted on: 04 03 2026.

Vibe Coding Experience at Comtrade 360

Where It Shines, Where It Struggles, and How to Use It Safely

„Vibe coding“ tools – natural-language, sketch-to-app, and prompt-to-prototype systems – promise to turn ideas into working software at surprising speed. Used well, they can unblock discovery and accelerate feedback. Used carelessly, they can create unreviewable systems, hidden risks, and production headaches.

Here’s a pragmatic perspective from the trenches: what they’re good for, where they fall short, how to review their output, and a sober bottom line.

What Vibe Coding Is (and Why People Like It)

At Comtrade 360, we actively use AI coding assistants and „vibe coding“ tools in real-world projects – primarily GitHub Copilot (including CreateMVPs), Cursor, and Lovable AI.

Vibe tools make software appear quickly. They translate prompts and high-level intent into UI scaffolds, API stubs, and glue code – particularly useful for:

• Prototyping and exploratory work. Rapidly testing flows, exploring UX variants, and validating concepts with stakeholders.
• Early-stage design. Moving from whiteboard to clickable paths in hours, not weeks.
• Democratizing development. Enabling non-engineers to participate meaningfully in shaping the first iteration.

This is the upside: faster iteration cycles, smoother on-ramps for product owners and architects, and earlier end-user feedback – reminiscent of the early promise of low-/no-code platforms.

Where Vibe Coding Falls Short

Its strengths are also its limits:

• Unpredictable and difficult to explain. Many vibe-generated systems are not easily reproducible or explainable. When bugs surface, debugging can become guesswork.
• Edge-case failures and security gaps. „Happy path“ demos often conceal edge-case crashes, privilege escalation paths, API leaks, weak authentication, exposed Personally Identifiable Information (PII), and unencrypted data. Built-in security checks exist—but they tend to be shallow and miss context-specific risks.
• Difficult auditing. If you cannot clearly audit how a feature works, it should not be trusted in production – especially in safety-critical or regulated environments.
• Scaling barriers. Many tools lack robust data models, granular access controls, and enterprise-grade governance. They excel at prototypes but frequently hit a wall at scale.

Enterprise reality: When handling health data, financial transactions, or critical infrastructure, „shrug and try again“ is not an option. Production systems require comprehensive unit testing, rigorous code reviews, clear ownership, and long-term maintainability. Relying on AI-generated tests to validate AI-generated code is a circular bet – senior engineers must still understand and validate every line.

Some ecosystems (e.g., Java in large enterprises) are intentionally designed for longevity, stability, and discipline—not rapid, throwaway experimentation.

Mission-critical caveat: Do not delegate core infrastructure or safety-relevant logic to a vibe tool. Use it at the edges; keep the core in experienced hands.

Team Fit and Tooling Reality

Vibe tools target different personas and skill levels. Developer experience, extensibility, security posture, and compliance features vary widely.

In regulated environments with strict development and infrastructure standards, that disparity typically confines vibe coding to prototype duty – valuable, but not a replacement for a disciplined SDLC.

Also, be mindful of intellectual property and terms of use. Understand who owns generated artifacts and how platforms process and store your data. Involve legal stakeholders before sensitive prototyping.

Best Practices: How to Use Vibe Coding Without Burning Yourself

Treat vibe output as a first draft – not a final product. Establish strong guardrails.

1) Review Is Non-Negotiable

• Treat AI output like a junior developer’s PR. Require senior or domain-owner review before merging.
• Embed static analysis, linting, SAST/DAST, secret scanning, and license checks into CI for every change – especially AI-generated code.

2) Test With Intent (Including AI-Assisted Tests)

• Use AI to generate unit tests, fixtures, and test data – but review those tests with the same rigor as production code.
• Expand coverage to edge cases, not just happy paths. Assume the prototype missed them.

3) Separate Analysis From Implementation

• Use one AI session to analyze the existing codebase (map risks, dependencies, contracts).
• Use a separate session to propose a modification plan or draft a PRD suitable for a human engineer. This separation helps surface inconsistencies that might be overlooked in a single conversational flow.

4) Keep “Core” vs. “Non-Core” Boundaries Explicit

• Allow AI to assist with well-documented interfaces, self-contained routines, and non-mission-critical features.
• For core capabilities – anything that could wake up your on-call engineer at 3 a.m. – humans lead, AI assists.

5) Shift Security Left—Seriously

• Do not bolt security on at the end. Define threat models, authentication/authorization rules, data handling policies, and logging requirements before prompting.
• Enforce access controls and data minimization in the design – not after the demo.

6) Governance and Traceability

• Ensure every artifact – human- or AI-generated – can be validated and traced through the pipeline: authorship, checks passed, residual risks.

7) Know the Legal Details

• Verify IP ownership and platform terms of use upfront, especially when working with proprietary or regulated content.

What They’re Good For (Use With Confidence)

• Prototypes and MVPs. Rapid exploration of functionality and UI/UX directions with real feedback.
• Concept comparisons. Quickly testing multiple approaches and keeping the best.
• Non-critical add-ons. Reports, internal dashboards, and glue code around well-documented APIs.
• Early team alignment. Facilitating collaboration between architects, product managers, and engineers.

What They’re Not Good For (Use With Caution or Avoid)

• Mission-critical code and core infrastructure.
• Regulated, safety-critical, or high-assurance domains—unless heavily refactored and independently re-verified.
• Long-lived systems requiring strict maintainability and auditable traceability.
• Invisible security layers (authentication/authorization, cryptography, PII handling), where mistakes are costly.

Field Notes: A Sensible Workflow

This approach preserves creativity and speed without gambling on reliability.

Bottom Line

Vibe coding is an evolution, not a revolution. It lowers the barrier to prototyping, broadens collaboration, and accelerates early discovery. At the same time, it decentralizes risk: more generated code means more to review, secure, and maintain.

Used as a prototyping method, it is powerful. Used as a direct path to production, it is risky, unless surrounded by disciplined engineering practices.

AI vibe coding tools are excellent for rapid prototypes, greenfield experimentation, and conversational refactoring. They are not yet a turnkey path to production-ready enterprise systems with built-in governance and security.

Can we trust software written by AI enough to operate an airplane?

It depends.

Are there humans on board?