it can generate a lot of code quickly. Using the likes of Cursor or Claude Code, you can quickly develop powerful and efficient applications. However, in most cases, the initial code generated by these models is not ready for full production.
There are many errors and imperfections in the code that can have a big impact when you try to deploy the code to production. Therefore, you need to use some techniques to make sure that the code you generate with Claude Code is ready for production.
In this article, I will discuss how to ensure that the code generated by Claude Code is ready for production and validates our business in a developed application.
Why generate code with Claude Code
First, we need to discuss why you should generate code with coding agents like Claude Code. The main reason you should just do it is to save a lot of time. Handwriting code is, of course, much slower. The next level of agent coding is to have tab completion, where you start writing parts of the code, and the AI completes it for you.
However, even the completion of time seems to slow down once you start developing with fully functional systems. The reason for this is that you can easily define what you want to build or achieve, and the coding agent can build it for you.
Yes, you get less control over the actual code, but with the latest programming models, such as Claude Opus 4.6, the models are able to produce code that looks like what a human could write.
You should be generating code with coding agents simply because it saves you a lot of time and because coding agents are capable enough to generate good code that will work well when sent to production, given that you take the right precautions, which I will discuss in the next section.
How to ensure that the code you generate is ready for production
There are two main aspects to ensuring that the code is ready for production. One is to provide the right input and feedback to Claude Code when it does its first iteration of the code to ensure that the code is as robust and production-ready as possible.
The second step is to have a review function where you look at the originally developed code, review it, and decide what needs to be fixed in order to use the code in production.
I will write one paragraph for each step:
Improving stability in the original code
This step is important because this is where you generate the first version of the code, and you, of course, want the generated code to be as good as possible. Now we need to ensure that the initial code generated by the agent is correct and ready for production, as it is ready for production. It is also important to consider here that the first code generated will have a significant impact on the architecture. It is difficult to completely change the structure by repeating the code. An architectural framework is usually created with the first iteration of the built code. If you want to change the structure later, you usually need to start from scratch, generating completely new code.
The main point of how I improve stability and production readiness in the original code is this:
- I updated the Claude.md and Agents.md files in all my collections, telling Claude Code how to code and what to watch out for.
- I'm an avid user of program mode, where I make sure I spend enough time programming with my coding agent before starting to use. This is important to make sure that the code agent is actually running the program I have in mind
- You give clear instructions to the agent and make sure you understand the problem you are dealing with. And give the agent all the context they need to make good decisions
Regarding the point about the Claude.md and Agents.md files, you can achieve this by always making sure to update those files whenever you change some code in the repository. So when you implement a new feature or fix a bug, you complete that task and make sure you tell the agent to generalize the information from the music where you used the debug feature and write it down in the Claude.md or Agents.md files.
On the second point about using program mode, this is very important because you need to make sure that the agent understands your vision. Part of the challenge with writing natural language commands instead of typing code is that you don't specify what you want to build. Of course, writing code is the last level of specificity because you are writing logic. However, this does not work well enough in nature, which is why we use natural language. However, when we use natural language, we must also face the challenge of clearly stating what we want to build. This is where program mode comes in handy because it allows the agent to ask you questions like clarifying questions to make sure you understand the task at hand.
Finally, it is very important to give clear instructions to the agent, and you need to make sure you understand the job yourself. If you do not understand the task yourself, it is very difficult to explain to the agent how to solve the task properly. In addition, it is very important that the agent has enough context. If it needs to read news on line or messages in Slack, it's important to give the agent access to this information so they can make the best decisions for themselves.
Improving resilience through repetition
Once you're here, you've already implemented the first part of the code, and now you need to do some iterations to make sure the code is ready for production and working as intended.
I want to highlight two points that I make at this stage of my development. The first point, of course, is that I thoroughly test the implementation. I spend a lot more time, relatively speaking, testing the code my agents have built compared to before. Yes, this is because the coding part of the implementation has been sold by the coders, while the testing part is still important for people to participate in to make sure the implementation is working as intended.
So, even if you feel like you're spending a lot of time testing code, I often think it's worth it because the development process is still more efficient than ever. We just spend a lot of time testing the code because the implementation part has worked so well.
Second, I want to point out that I have a separate setup for the PR review capability on my computer. I can simply tell my Claude Code to use the pull request review capability, and it will read from any PR or code I've generated and make sure it's ready for production. What makes this skill even more powerful is that I've provided the skill with information about the cache where people have made mistakes before, and the code wasn't working as intended.
This could be some cases of people using something that went wrong when deployed to production, or some security measures that should be taken when writing code in a particular repository.
The conclusion
In this article, I discussed how to create production-ready code with Claude Code. I've included two key points on how I make sure the code I generate with my coding agents is ready for production. The first point is that I am careful when making the first versions of my code by actively using the Claude.md files, using program mode, and giving clear instructions to my agent. Second, I improve code production readiness by iterating the code through testing and the ability to review the pull request. This presents a lot of problems, so I avoid sending bugs to production. Going forward, I think having these tools that I described in this article to ensure that the generated code is ready for production will be incredibly important. There's no way every developer has enough time to fully review pull requests, given the speed at which we build code with coding agents. We need to use coding agents to review code as well, and not just code.
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