AI coding tools can build working applications remarkably quickly, but they do not reliably produce production-ready software on their own. Generated code often works on the happy path while missing permission checks, input validation, error handling, secure configuration, tests, monitoring and a repeatable deployment. With experienced engineers reviewing, testing and hardening the output, AI-assisted development can produce production-quality software much faster than before.
At Visnoa Systems, AI-assisted development is central to how we work. That is exactly why we are careful about what it can and cannot do. This article is our honest view.
What AI coding tools are genuinely good at
- Scaffolding: generating project structure, screens, forms and standard components in minutes;
- boilerplate: data models, API endpoints, validation schemas and configuration;
- translation: turning a clear description or a design into a first implementation;
- exploration: trying several approaches quickly before committing to one;
- drafting tests and documentation for code that already exists;
- explaining unfamiliar code and suggesting fixes for specific errors.
For prototypes and the routine parts of MVPs, this can reduce build time dramatically.
Where AI-generated applications commonly fall short
When we review applications built quickly with AI tools, the same issues come up again and again:
Security and permissions
The app checks that a user is logged in, but not whether they are allowed to see this record. Changing an ID in the URL reveals someone else’s data. API keys sit in the code repository. These are among the most serious and most common problems.
Validation and error handling
Input is validated in the browser but not on the server. A failed network call leaves the interface stuck. Errors show technical messages to users — or nothing at all.
Data integrity
Two people editing the same record overwrite each other. A payment succeeds but the order is not saved. There are no database migrations or backups.
Consistency over time
Code generated in many separate sessions can drift: three different ways of doing the same thing, duplicated logic and unused files. It works, but becomes harder to change safely.
Operations
The app runs on one machine but there is no documented deployment, no staging environment, no logging, no alerts and no way to know when something breaks.
Why this happens
AI tools respond to what they are asked. Prompts naturally describe features — “add a dashboard that shows orders” — rather than everything that should not happen. Production readiness is largely about the things that should not happen. That requires someone to think about them deliberately.
A production-readiness checklist for AI-built apps
- Every server endpoint checks both authentication and authorisation.
- All input is validated on the server.
- Secrets live in environment variables or a secrets manager, not the repository.
- Dependencies are current and free of known critical vulnerabilities.
- Errors are handled gracefully and logged with useful context.
- Critical user journeys have automated tests.
- Database changes use migrations; backups run automatically and have been test-restored.
- Deployment is scripted and repeatable, with separate staging and production.
- Uptime and error monitoring alert a real person.
- There is documentation another developer could follow.
If several of these are missing, your application is a prototype or early MVP — which may be exactly right for your stage, as long as everyone knows it.
How we use AI responsibly at Visnoa
- An engineer reviews and runs every change before it is merged.
- Architecture, data models and permissions are designed deliberately, not generated ad hoc.
- We test real user journeys and the failure cases, not just the demo path.
- We never describe a prototype as production-ready.
- We do not put client data into AI tools without agreement.
Already have an AI-built app with real users? Our prototype hardening service starts with a code review and a prioritised risk report.
Frequently asked questions
Will AI replace software developers?
It is changing what developers spend their time on — less typing, more reviewing, designing and testing. Judgement about what to build and how to make it reliable remains essential.
Is AI-generated code less secure?
Not inherently, but it is easy to generate insecure code without noticing, especially around permissions and secrets. Review and testing close most of that gap.
Can I keep using AI tools after you harden my app?
Yes. We document the structure and conventions so future AI-assisted changes are easier to keep consistent and safe.
