AI coding tools have made it incredibly easy to build and launch software.

With tools like Cursor, Claude Code, and GitHub Copilot, developers can now turn ideas into functional applications in days, sometimes even hours.

But here's the problem: Getting your application to work is not the same as making it production-ready.

Your application might work perfectly on your machine. The signup page loads, the database connects, payments go through, and the AI features respond as expected.

But what happens when real users start interacting with it?

What happens when someone discovers an exposed API key, sends thousands of requests to your endpoints, accesses another user's records, or triggers an unexpectedly large AI bill?

This is where many developers make a mistake. They focus on building features and forget about application security, performance, reliability, and production readiness.

If you're using AI coding agents to build software, this 20-point vibe coding pre-launch checklist should be part of your deployment process.

Before launching your next AI-built application, give your coding agent this prompt.

The 20-point pre-launch checklist for AI-built applications

1. Remove exposed API keys and secrets

Move API keys, database credentials, and other sensitive information out of frontend code.

  • Store secrets in environment variables or a secure secrets manager.
  • Ensure .env files and other sensitive files are excluded from Git.
  • Never expose private API keys in browser requests or frontend bundles.

Remember, anything shipped to the browser should be considered accessible to users.

2. Rotate compromised credentials

If an API key, password, or secret has ever been committed to a Git repository, consider it compromised.

Removing it from the latest commit is not enough. Revoke the exposed credential and generate a new one.

Also review your repository history and deployment environments to ensure old credentials are no longer in use.

3. Implement rate limiting

Protect your API endpoints against excessive requests and abuse.

  • Set reasonable request limits per user or IP address.
  • Apply stricter limits to expensive operations.
  • Protect authentication, OTP, payment, and AI endpoints.
  • Consider throttling repeated failed login attempts.

Without rate limiting, one user or automated bot could generate unnecessary infrastructure and third-party API costs.

4. Enforce authentication and authorization on the backend

Never rely on frontend checks to protect sensitive functionality.

Hiding an admin button in the UI does not mean unauthorized users cannot access the underlying endpoint.

The backend must independently verify who is making a request and whether they have permission to perform that action.

Authentication establishes who the user is. Authorization determines what they are allowed to do.

5. Secure database access

Ensure users can only access records they are authorized to see.

  • Implement Row-Level Security (RLS) where supported.
  • Enforce tenant isolation in multi-tenant applications.
  • Never trust user IDs supplied by the frontend without verification.
  • Use parameterized queries to prevent SQL injection.
  • Restrict database permissions based on application requirements.

This is particularly important when building applications with AI coding agents that interact with production databases.

Related reading: How to Safely Give AI Agents Access to a Production Database.

6. Validate and sanitize user input

Validate every input on the backend, even if frontend validation already exists.

Protect against:

  • SQL injection.
  • Cross-Site Scripting (XSS).
  • Malicious file uploads.
  • Unexpected data types and oversized payloads.
  • Invalid API parameters.

Never assume that data coming from your frontend is trustworthy.

7. Set spending limits for external services

AI APIs, cloud infrastructure, SMS gateways, email services, and other third-party integrations can become expensive.

Configure usage limits, budget alerts, quotas, and spending controls wherever supported.

Pay particular attention to AI API usage. An exposed endpoint that triggers expensive model calls can quickly become a financial problem.

8. Handle errors safely

Never expose internal stack traces, database queries, server paths, or sensitive information to users.

Return meaningful error messages while keeping detailed diagnostic information in your server logs.

For example, instead of showing users a raw database exception, display:

"Something went wrong while processing your request. Please try again later."

Your developers should still have enough information in the logs to investigate the actual problem.

9. Set up error tracking and monitoring

You should know when your application breaks before your users start complaining.

Integrate tools such as Sentry or an equivalent monitoring solution to capture:

  • Application exceptions.
  • Failed API requests.
  • Performance issues.
  • Unexpected application behaviour.
  • Critical background job failures.

Monitoring helps you identify and resolve problems before they affect too many users.

10. Back up your database and test recovery

Having a database backup is not enough.

Verify that your backups are working and periodically test restoring them.

Consider:

  • Automated database backups.
  • Backup retention policies.
  • Point-in-time recovery where supported.
  • Secure backup storage.
  • Documented restoration procedures.

A backup you have never tested is an assumption, not a recovery strategy.

11. Build proper 404, 403 and 500 error pages

Your application should handle errors gracefully.

Create proper pages for:

  • 404: Page not found.
  • 403: Access forbidden.
  • 401: Unauthorized access.
  • 500: Internal server error.

Do not leave users staring at a blank page or raw server error.

Your error pages should help users understand what happened and what they can do next.

12. Test on low-end mobile devices

Not everyone has the latest iPhone or Samsung flagship.

Test your application on affordable Android devices, slower networks, and smaller screens.

Pay attention to:

  • Responsiveness.
  • Loading times.
  • Memory usage.
  • Form usability.
  • Navigation.
  • Mobile payment flows.

An application that works perfectly on your development machine may perform very differently on a low-end device.

13. Optimize application performance

Aim for pages that load quickly, ideally within three seconds under realistic conditions.

However, do not treat three seconds as a universal performance benchmark. Measure actual performance using tools such as Google Lighthouse and Core Web Vitals.

Optimize:

  • Images and other static assets.
  • Database queries.
  • API response times.
  • JavaScript bundles.
  • Unnecessary network requests.
  • Caching strategies.

Performance is part of the user experience, not something to consider after launch.

14. Implement SEO and social sharing metadata

Make sure search engines and social platforms can understand your pages.

Include:

  • Appropriate page titles.
  • Meta descriptions.
  • Canonical URLs where necessary.
  • Open Graph metadata.
  • X (Twitter) card metadata.
  • Properly sized social sharing images.

Your links should look professional when shared on WhatsApp, LinkedIn, X, and other platforms.

Also ensure that important public pages can be discovered and indexed by search engines.

15. Add a privacy policy and terms of service

If you collect user information, explain what you collect, why you collect it, how you use it, and how users can exercise their rights.

Include appropriate terms of service covering how people can use your application.

Make sure these documents reflect your actual practices and applicable legal requirements.

Do not simply copy another application's privacy policy.

16. Set up product analytics

You need to understand how people interact with your application.

Track important events such as:

  • User registrations.
  • Successful logins.
  • Feature usage.
  • Subscription upgrades.
  • Failed transactions.
  • User drop-offs.

Tools like PostHog, Google Analytics, or Microsoft Clarity can help you understand user behaviour.

Remember to respect user privacy and applicable consent requirements.

17. Test critical user journeys end to end

Do not just test individual buttons.

Test complete user journeys, including:

  • Account registration.
  • Email verification.
  • Login and logout.
  • Password reset.
  • Payments and subscription activation.
  • Failed payment handling.
  • Account deletion where applicable.

Test both successful and unsuccessful scenarios.

For example, what happens when a payment provider sends the same webhook twice? Does your application process the payment twice?

Your payment flows should be idempotent, meaning repeated requests should not cause unintended duplicate transactions.

18. Configure transactional emails properly

Your password reset emails, verification links, receipts, and notifications should actually reach users.

Configure:

  • SPF.
  • DKIM.
  • DMARC.

Also monitor delivery failures, bounces, and spam complaints.

A password reset feature is useless if its emails consistently land in spam.

19. Provide a reliable support channel

Users should have a way to reach you when something goes wrong.

Provide a support email, contact form, or another reliable communication channel.

Make sure someone is actually monitoring it.

Do not launch an application with no way for users to report problems or request assistance.

20. Prepare a rollback and recovery plan

What happens if your deployment breaks production?

Before launching, document:

  • How to roll back to the previous stable version.
  • How to restore the database if necessary.
  • How to disable problematic features.
  • How to handle failed database migrations.
  • Who is responsible for responding to incidents.

Never assume you will figure everything out during an outage.

The actual prompt to give your AI coding agent

You can use the following prompt with Cursor, Claude Code, or another AI coding agent before deploying your application.

I am preparing this application for production launch.

Act as a senior software engineer, application security engineer, and DevOps engineer.

Perform a comprehensive pre-launch review using the 20-point checklist in this article.

For every item:

  1. Inspect the existing implementation before making changes.
  2. Identify what is already implemented and what is missing.
  3. Explain the risks associated with each gap.
  4. Implement the necessary improvements without unnecessarily rewriting working functionality.
  5. Write and run relevant tests to verify the changes.
  6. Do not claim something is secure or production-ready without evidence.
  7. Identify anything that requires manual configuration, external verification, or infrastructure access.
  8. Never perform destructive production operations without explicit approval.

At the end, provide a report showing each checklist item, its implementation status, the files changed, tests executed, remaining risks, and recommended next steps.

Do not deploy the application until I explicitly approve it.

One more thing: Don't blindly trust your AI coding agent

Giving Claude Code or Cursor this checklist is a good starting point, but there is something important to remember.

An AI agent can tell you that it has implemented a security measure without actually implementing it correctly.

For every item, ask the agent to explain its implementation and show you how it verified the result.

For example, asking an agent to implement rate limiting is not enough. You should verify that the limits are enforced on the server and cannot be bypassed by changing frontend requests.

The same applies to authentication, database permissions, backups, and payment processing.

This is also why understanding the top 5 places to implement guardrails in an AI agent is important when building applications that rely on autonomous AI systems.

Final thoughts

The funny thing is that this checklist isn't particularly complicated. These are fundamental software engineering and production-readiness practices.

But in the excitement of building something quickly with AI, it is easy to forget them.

Vibe coding has lowered the barrier to building software. It has not lowered the responsibility of putting secure, reliable software into production.

Build fast. Experiment. Use AI agents. Automate as much as possible.

But before you share that production URL, make sure you can confidently answer one question:

Is this application ready for real users, real data, real money, and real attacks?

And remember, your responsibility as an engineer does not disappear because an AI agent wrote the code.