AI Coding Agents Are Getting Better at Building Apps — Here's What Still Goes Wrong in Production

Quick Answer

AI coding agents can build applications in minutes. The code works on the developer's machine. The deployment fails. The familiar problems are not new — they are the same deployment issues developers have always faced. What has changed is that the code is now generated by AI, and the deployment needs the same careful review as any other code.

The gap between an AI-generated application and a production-ready application is not about AI capabilities. It is about the familiar fundamentals of deployment: environment configuration, secrets, authentication, dependencies, domain setup, HTTPS, and error handling.

The Core Problem

Local environment is not production environment.

An AI coding agent builds a React application with a backend. Everything works on the developer's machine. The developer deploys it. Production fails. The most common reason is that local development conditions do not match production conditions.

• Different environment variables.
• Different API URLs.
• Different database configurations.
• Different authentication settings.
• Missing build steps or incorrect build commands.
• Wrong Node or Python versions.
• Missing static assets or incorrect paths.
• Incorrect domain configuration.
• HTTPS issues.
• Caching problems.
• Insufficient error logging.

Missing Environment Variables

AI coding agents often assume environment variables exist that were never defined for production. Every variable the application needs must be defined in the production environment. Create a checklist of all required variables and verify each one.

Incorrect API URLs

AI-generated code may use localhost or development API URLs. Production requires the correct live API URLs. Verify every API endpoint the application calls.

Broken CORS

Cross-origin resource sharing must be configured correctly for the production domain. If the frontend and backend are on different domains, CORS must allow requests from the production origin.

Incorrect Database Configuration

Database connection strings, pool sizes, and migration scripts must be configured for the production environment. AI-generated code may use a local database connection that does not work in production.

Dependency Conflicts

AI-generated code may include dependencies that conflict with each other or with the project's existing packages. Review the dependency tree and remove unnecessary packages.

Missing Migrations

If the application uses a database, ensure all migrations have been applied. AI-generated code may include migration files that were never run in the production environment.

Authentication Mistakes

Authentication must work correctly in production. Test with real credentials. Verify session handling, token expiry, and password recovery. Check that authentication works on the production domain, not just localhost.

Exposed Secrets

Search the project for private credentials. AI-generated code may accidentally include API keys, database URLs, or other secrets in source files, environment configurations, or client-side bundles.

Incorrect Build Commands

Verify the production build command is correct for the framework and hosting platform. Run the build and verify the output directory contains the expected files.

Wrong Node or Python Versions

The production server must use the correct runtime version. AI-generated code may specify a version that differs from the production environment.

Missing Static Assets

Verify that all images, CSS, JavaScript, fonts, and icons are present in the build output. A website can look perfect locally and lose assets after deployment because of incorrect paths or build configuration.

Incorrect Domain Configuration

Before launch, confirm the root domain, www hostname if used, DNS configuration, and hosting project mapping. Make sure the domain points to the intended website.

HTTPS Issues

Open the production website using HTTPS. Check the certificate, correct hostname, redirect behavior, and mixed-content warnings. Do not assume domain connection automatically means HTTPS is correct.

Caching Problems

Production caching may serve outdated content or cause unexpected behavior. Verify that caching is configured correctly for the application.

Insufficient Error Logging

If the application fails in production and there are no logs, debugging is nearly impossible. Confirm that error logging and monitoring are in place before deploying.

Before You Trust an AI Coding Agent With Deployment

Run through this checklist:

• Review the repository thoroughly.
• Review all dependencies.
• Review all environment variables.
• Review all secrets and credentials.
• Run the production build.
• Deploy to a staging environment first.
• Verify production permissions.
• Confirm logs and monitoring.
• Create a rollback plan.
• Test as a new user, not a logged-in developer.

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