An honest assessment of vibe coding for business software: what it does well, where it breaks under production requirements, and how to use it without accruing risk.
Vibe coding for business software describes building applications by describing intent to an AI coding tool and accepting largely unreviewed output. It has produced genuinely useful results for prototypes and internal tools, and it produces predictable problems when the output becomes something a business depends on.
The useful question is not whether to use it, but where the boundary sits.
What it does well
Prototypes and proofs of concept, where the goal is to test an idea and the code is disposable. Speed matters, correctness at the margins does not, and throwing the result away is the plan.
Internal tools with small user counts, low consequence, and no sensitive data. A script that reformats a report or a dashboard three people use is a reasonable target.
Exploratory work in unfamiliar territory, where generated code serves as a starting point for understanding an API or approach.
One-off data tasks, migrations, and analysis where the output is verified by inspecting the result.
Where it breaks
Security. Generated code frequently omits authorization checks, trusts user input, exposes data across tenant boundaries, and handles secrets carelessly. These faults are not visible from the working behavior of the application, which is why they survive to production.
Data correctness. Subtle errors in aggregation, currency, timezone, and null handling produce plausible wrong numbers. Business software that reports wrong figures confidently is worse than software that fails.
Concurrency and state. Race conditions and missing idempotency rarely appear in testing and appear reliably under real load.
Maintainability. Code assembled without a consistent structure becomes expensive to change. The cost arrives later, which is why it is systematically underestimated at the point of decision.
Compliance and auditability. Regulated processes need documented controls, traceable decisions, and evidence. Code nobody reviewed cannot supply that.
A workable boundary
Use the following test. If the software handles personal data, money, safety, or regulated processes, or if more than a handful of people depend on it, or if it will be maintained for more than a few months, then generated code needs the same review, testing, and security scrutiny as any other code.
Below that line, accept the output and keep it disposable.
The failure mode to avoid is drift: a prototype that quietly becomes the system of record without ever passing through review. Decide explicitly when something crosses the line, and rewrite or harden it deliberately at that point.
Using AI coding tools well on serious work
The productive pattern on production software is not unreviewed generation but assisted development: generating a first draft that a competent engineer reviews and reshapes, writing tests, explaining unfamiliar code, and handling mechanical refactors.
Keep the human accountable for the design. Models produce locally plausible code that fits poorly into a larger architecture, and only someone holding the whole design can catch that.
Review generated code against a specific checklist: authorization on every entry point, input validation, tenancy isolation, error handling, secret management, and data correctness in aggregations. These are where generated code fails most consistently.
Require tests, and check that they test behavior rather than restating the implementation.
Frequently asked questions
Is generated code inherently insecure?
Not inherently, but it is frequently incomplete on authorization and validation because those requirements are implicit rather than stated in the prompt.
Can non-developers build production systems this way?
They can build working demonstrations. Making one safe for business use requires the judgment that reviewing it demands.
How do we stop prototypes reaching production?
Define an explicit promotion gate with review, testing, and security requirements, and enforce it before anything handles real data.
Does this reduce the need for engineers?
It shifts effort from typing toward design, review, and verification. On systems with real consequences, the review burden is substantial.
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Written by
Moueen Togarvi
Founder & CEO at Voquarn Code, focused on product engineering, search growth, and practical AI systems.