Production-Ready: Shipping AI-Generated Code Without Losing Sleep

We’ve reached the final stage. Over the last three articles, you’ve set up your workspace, mastered context windows, and orchestrated a team of specialized AI agents to build parallel features. You have an app that compiles perfectly, runs smoothly on localhost, and looks fantastic.

But let’s be brutally honest: “It works on my machine” is not a deployment strategy—especially when an AI wrote the code.

When you push AI-generated code to production, the stakes change fundamentally. Large Language Models (LLMs) are incredibly capable, but they are also confident bullshitters. They will happily write a beautifully optimized sorting algorithm while simultaneously introducing a critical SQL injection vulnerability or hardcoding a temporary API key.

Shipping AI code requires a massive shift from a generative mindset to an adversarial one. Here are the exact problems you will face when moving from prompt to prod, and the automated pipelines you need to solve them.

1. The Happy-Path Trap

The Problem: AI models are optimists. They assume APIs will always return 200 OK, users will always input perfectly formatted email addresses, and databases will never lock. If you ask an agent to write a feature, it will write the “happy path” code. If you ask it to write tests after the fact, it will write tests that simply validate its own flawed, happy-path logic.

The Solution: Force the AI into Test-Driven Development (TDD).

An AI agent is actually a much better QA engineer than it is a feature developer, provided you constrain it. Flip the ratio: use your agents to write 90% of the tests and 10% of the boilerplate.

• Step 1: Write your feature requirements in a clean Markdown file (feature-spec.md).
• Step 2: Open a Cursor chat with your QA persona (@.cursorrules-qa) and prompt: “Act as a chaotic QA engineer. Read feature-spec.md and write comprehensive Jest/Vitest unit tests. Focus exclusively on null inputs, malformed JSON, and network timeouts. Do NOT write the implementation code.”
• Step 3: Once you have failing tests (the red phase), switch to your Backend Agent. Point it at the failing test file and say: “Write the minimal implementation required to make these tests pass.”

2. The Visual Deception

The Problem: AI writes code that looks incredibly idiomatic and clean. Because the formatting is flawless and the variable names make sense, your brain naturally glazes over during a visual PR review. You merge it, and it immediately breaks in production because of a subtle logical flaw hidden beneath perfect syntax.

The Solution: The Unforgiving CI/CD Pipeline.

You cannot rely on your eyes anymore. Your Continuous Integration pipeline must be deterministic and ruthless. If an AI agent refactors a component and drops your test coverage below 80%, the pipeline halts. If it introduces an un-typed any in TypeScript, the pipeline halts.

Here is the baseline GitHub Action workflow you need:

name: AI Production Safeguards
on: [pull_request]

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Setup Node
        uses: actions/setup-node@v4
        with:
          node-version: '20'
          
      - name: Strict Install
        run: npm ci # Ignores AI-hallucinated package.json tweaks
        
      - name: Aggressive Linting
        run: npm run lint:strict # Fails on ANY warning
        
      - name: Type Checking Gate
        run: npm run tsc --noEmit # Catches AI TypeScript hallucinations
        
      - name: Test Coverage Gate
        run: npm run test:coverage -- --coverageThreshold='{"global":{"branches":80,"functions":80,"lines":80}}'

3. The Hallucination Hazard

The Problem: AI agents have read millions of lines of GitHub code. This means they know millions of solutions, but they’ve also memorized millions of security flaws. Worse, they frequently hallucinate library names (e.g., suggesting npm install fast-react-router when that package doesn’t exist). Malicious actors register these hallucinated package names and fill them with malware—a classic supply chain attack.

The Solution: Strict Dependency Gates and Red Teaming.

1. Never let AI install packages autonomously: Always verify a dependency exists, is well-maintained, and has actual weekly downloads on NPM or PyPI before allowing it into your package.json.
2. The Red Team Prompt: Before merging a critical PR (like authentication), open a fresh context window. Feed the diff to the agent and prompt: “Act as a malicious security auditor. Review this diff against the OWASP Top 10. Identify any potential vectors for Cross-Site Scripting (XSS) or Insecure Direct Object References (IDOR). Do not praise the code. Only output vulnerabilities.”

4. The Token Burn

The Problem: You are orchestrating a team of agents. If you use the heaviest, smartest, most expensive model to write every single JSDoc comment or translate a CSS file to Tailwind, you will burn through your token limits and API budget in a matter of hours.

The Solution: Strategic Model Routing. You need to optimize your AI usage based on the complexity of the task.

The AI landscape changes practically every Tuesday, so timestamping your stack is crucial. As of February 2026, here is the model routing strategy I use to balance pure reasoning power with API costs:

By explicitly telling your CI pipelines or orchestration scripts which model to call for which task, you keep your agents running fast and your API bills shockingly low.

5. Real Case Study: Shipping a Webhook Microservice

Let’s look at how this methodology comes together in the real world. I needed to build a Webhook Processing Microservice for ezablocki.com to receive payloads from Stripe and update a Postgres database.

1. The Guardrails: Before the AI wrote a single line of Express code, I used the QA Agent to generate tests for invalid Stripe signatures.
2. The Catch: The Backend Agent mapped the Stripe payload incorrectly. Because I had tests written first, the error was caught in milliseconds locally.
3. The Deployment: I fixed the mapping logic and pushed to GitHub. My automated pipeline took over, verified strict TypeScript types, confirmed 90% test coverage, and only then deployed the Docker container.

It has been running in production for months without a single dropped webhook.

Conclusion: The Future is Editing, Not Typing

If you’ve made it through this four-part series, your relationship with coding has fundamentally changed. You understand that the true power of AI lies in context management, orchestration, and rigorous validation.

We are entering an era where writing syntax is no longer the bottleneck. The new bottlenecks are system architecture, requirement definition, and automated quality assurance. By adopting the Tech Lead mindset and building unforgiving deployment pipelines, you are positioning yourself ahead of the curve.

The code is cheaper than ever to write. The value is in knowing how to safely ship it.

Social Media Promo Post (LinkedIn / X)

“It works on my machine” is a terrible deployment strategy. When an AI wrote the code, it’s a disaster waiting to happen. 🚨

AI writes code that looks incredibly clean. Your brain glazes over during the visual PR review, you hit merge, and it immediately breaks in production because of a subtle logic flaw hidden beneath perfect formatting.

To ship AI-generated code safely, you have to shift from a generative mindset to an adversarial one.

In the final part of my AI Engineering series on ezablocki.com, I break down exactly how to move from prompt to prod: 👉 AI-Assisted TDD: Make the AI write the failing edge-case tests before the implementation. 👉 Unforgiving Pipelines: Why your CI/CD must fail on a single dropped coverage point or missing TypeScript type. 👉 The Red Team Prompt: How to use Claude/GPT to audit your diffs for OWASP vulnerabilities. 👉 The Feb 2026 Model Stack: How to route tasks between heavy lifters (Sonnet/GPT-4o) and grunt workers (Haiku/Llama) to stop burning your token budget.

The code is cheaper than ever to write. The real value is knowing how to ship it.

Read the full production guide in the comments! 👇

#SoftwareEngineering #AI #DevOps #CICD #Cursor #WebDevelopment